Integrative Clinical Research (Advances in Experimental Medicine and Biology, 1375) [1st ed. 2022] 9783030996291, 9783030996307, 3030996298

Citation preview

Advances in Experimental Medicine and Biology 1375 Clinical and Experimental Biomedicine

Mieczyslaw Pokorski Editor

Integrative Clinical Research

Advances in Experimental Medicine and Biology

Clinical and Experimental Biomedicine Volume 1375 Series Editor Mieczyslaw Pokorski Institute of Health Sciences Opole University Opole, Poland

Science plays an essential role in bringing together clinical research and practice. The scope of this new subseries is multidisciplinary, dealing with the translation and application of biological and physiological research to clinical practice. The main fields cover physiology, molecular biology, biochemistry, and anatomy, as well as pharmacotherapy, including drug design and targeted treatment. Specific clinical areas include but are not limited to: Disorders and syndromes Cardiovascular pathophysiology Pulmonary medicine Inflammation and infection Disease biomarkers Disorders of immunity Genetics Metabolic and hormonal imbalance Hypoxia and neurodegeneration Aging process and stress disorders Psychosomatic medicine Public health and prophylaxis, rehabilitation medicine The subseries’s aim is to gather the ideas and share them with the scientific community as well as to disseminate and deliberate on the latest interdisciplinary medical knowledge, breakthroughs in new therapeutics and in translational medicine by publishing the most up-to-date scientific research. The articles will particularly focus on contemporary ideas and arguments concerning the core yet unsettled clinical issues. Being a blend of clinical investigation and practice the subseries is addressed to physicians, scientists, and allied health care professionals. Subseries of Advances in Experimental Medicine and Biology

More information about this series at https://link.springer.com/bookseries/5584

Mieczyslaw Pokorski Editor

Integrative Clinical Research

Editor Mieczyslaw Pokorski Institute of Health Sciences Opole University Opole, Poland

ISSN 0065-2598 ISSN 2214-8019 (electronic) Advances in Experimental Medicine and Biology ISSN 2523-3769 ISSN 2523-3777 (electronic) Clinical and Experimental Biomedicine ISBN 978-3-030-99629-1 ISBN 978-3-030-99630-7 (eBook) https://doi.org/10.1007/978-3-030-99630-7 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Artificial Intelligence and Precision Medicine: A Perspective . . . . . Jacek Lorkowski, Oliwia Kolaszyńska, and Mieczysław Pokorski Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jacek Lorkowski and Karolina Gawronska Quantum Medicine: A Role of Extremely Low-Frequency Magnetic Fields in the Management of Chronic Pain . . . . . . . . . . Giovanni Barassi, Mieczyslaw Pokorski, Raffaello Pellegrino, Marco Supplizi, Loris Prosperi, Celeste Marinucci, Edoardo Di Simone, Chiara Mariani, Alì Younes, and Angelo Di Iorio Manual Therapy Approach to the Extraocular Muscles in Migraine Treatment: A Preliminary Study . . . . . . . . . . . . . . . . Daniel Rodríguez-Almagro, Giovanni Barassi, Maurizio Bertollo, Esteban Obrero-Gaitán, Angelo Di Iorio, Loris Prosperi, Alexander Achalandabaso-Ochoa, Rafael Lomas-Vega, and Alfonso Javier Ibáñez-Vera Capacitive and Resistive Electric Transfer Therapy: A Comparison of Operating Methods in Non-specific Chronic Low Back Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giovanni Barassi, Chiara Mariani, Marco Supplizi, Loris Prosperi, Edoardo Di Simone, Celeste Marinucci, Raffaello Pellegrino, Vito Guglielmi, Alì Younes, and Angelo Di Iorio Serum Impedance in Children with Recurrent Respiratory Infections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gerard Pasternak, Katarzyna Pentoś, Deta Łuczycka, Maria Kaźmierowska-Niemczuk, and Aleksandra Lewandowicz-Uszyńska Olfactory Response to Altitude Hypoxia: A Pilot Study During a Himalayan Trek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Mazzatenta, Danilo Bondi, Camillo Di Giulio, and Vittore Verratti

1

13

23

29

39

47

55

v

vi

The Impact of Change in Hospital Admissions When Primary Care Is Provided by a Single Primary Care Physician: A Cohort Study Among HMO Patients in Israel . . . . . . Y. Fogelman, E. Merzon, S. Vinker, E. Kitai, G. Blumberg, and A. Golan-Cohen Risk of Falls in Patients Aged Over 65 in the Context of the Treatment Facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mariola Seń, Luba Jakubowska, Agnieszka Lintowska, Piotr Karniej, Barbara Grabowska, and Beata Jankowska-Polańska

Contents

63

69

Anthropometric Indices as Long-Term Predictors of Diabetes in Impaired Fasting Glucose Metabolism: Findings in the PURE Study in Poland . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agnieszka Święcicka-Klama, Katarzyna Połtyn-Zaradna, Maria Wołyniec, Andrzej Szuba, and Katarzyna Zatońska

79

Cardiovascular Risk Factors Drive Impaired Fasting Glucose to Type 2 Diabetes: Findings After a 9-Year Follow-Up in the PURE Study in Poland . . . . . . . . . . . . . . . . . . . . Agnieszka Święcicka-Klama, Katarzyna Połtyn-Zaradna, Maria Wołyniec, Andrzej Szuba, and Katarzyna Zatońska

89

Sleep-Disordered Breathing in Pregnancy . . . . . . . . . . . . . . . . . . . 101 Violetta Konstanty-Kurkiewicz, Edyta Dzięciołowska-Baran, Jacek Szczurowski, and Aleksandra Gawlikowska-Sroka

Adv Exp Med Biol - Clinical and Experimental Biomedicine (2022) 15: 1–11 https://doi.org/10.1007/5584_2021_652 # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Published online: 18 June 2021

Artificial Intelligence and Precision Medicine: A Perspective Jacek Lorkowski , Oliwia Kolaszyńska and Mieczysław Pokorski

Abstract

interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.

This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and

J. Lorkowski (*) Department of Orthopedics, Traumatology and Sports Medicine, Central Clinical Hospital of the Ministry of Internal Affairs and Administration, Warsaw, Poland Faculty of Health Sciences, Medical University of Mazovia, Warsaw, Poland e-mail: [email protected] O. Kolaszyńska Department of Cardiology, Independent Public Regional Hospital, Szczecin, Poland e-mail: [email protected] M. Pokorski Institute of Health Sciences, Opole University, Opole, Poland e-mail: [email protected]

,

Keywords

Artificial intelligence · COVID-19 · Genetics · Health care · Multi-omics · Precision medicine · Radiology

1

Introduction

Basic assumptions of cybernetics and robotics have been known to humanity for centuries. The Antikythera mechanism, a Greek device to display the calendar and celestial information, dated somewhere between 205 and 60 BC and designed most probably by Pythagoreans is considered the world’s oldest computer (Freeth et al. 2006). It was unraveled in a Roman shipwreck in 1901. The device was able to predict the phases of the moon, the movement of the planets, eclipses, and even social events such as the Olympic games. At 1

2

first, the device was left unnoticed, and it was fully appreciated no sooner than almost 100 years after the discovery, owing to the highresolution computed tomography (CT) imaging that has become available at the time (Pakzad et al. 2018). Another milestone was Al-Jazari’s creation of a robot able to strike cymbals in the twelfth century. Da Vinci’s sketches of a humanoid robot, dated to 1495, which were rediscovered in 1950, were the first to present a robot operated by pulleys and cables, a design that has inspired later robotic research. Jacques de Vaucanson of the eighteenth century and William Grey Walter of the twentieth century were the predecessors of the modern era of robotics. The Machina Speculatrix designed by William Grey Walter has had a particular influence on the current artificial intelligence studies. This inventor, investigating how the human brain works, showed that connections between a small number of neurons could lead to complex behaviors (Hamet and Tremblay 2017). A lot of innovative ideas were based on military technologies and applied in medicine and public health. In the twentieth century, an armament race took place, which accelerated the development of science in many fields, the influence of which is noticeable in the implementation of modern digital networks. A famous Enigma, the first cryptographic machine invented by Arthur Scherbius is a telling example of that (Bauer 2006). Alan Turing, considered the father of theoretical computer science, was the first to ponder the possibility of a thinking machine that he described in a paper “Computing machinery and intelligence” in 1950. Six years later, John McCarthy and Marvin Minsky coined the term artificial intelligence (AI) which remains in use by far in modeling science. Nowadays, the computational power gets stronger by the day. The human brain is capable of processing one million trillion transactions per second. A contemporary computer, for comparison, processes barely 93 thousand trillion transactions per second, and, according to the Moore law, computers will reach the power of the human brain in 2025 as they double the processing power every 18 months (Ergen 2019). Fractal dimensions of

J. Lorkowski et al.

biological systems require bigger computational power due to their iterated complexity that can be described better by the laws of quantum physics rather than classical physics. The first quantum computer, introduced by IBM-Q in 2019 with 20-qubit processors, has been already outdone by Google Sycamore (53-qubit processor) enabled to perform computations that would have taken 10 thousand years on the fastest supercomputer, in barely 3 min and 20 s (Arute et al. 2019). Inventions of digital networks and computers have begun to be widely used, finding applications in industry, medicine, and everyday life (Lorkowski et al. 2021a; Rankin 2018). This article aims to give a general overview of AI status and developments and how it connects to the way precision medicine is practiced.

2

Methods

We searched PubMed and Google Scholar databases to identify and collect articles pertinent to the interdependence of AI and precision medicine. The ending date of the search was 5 March 2021. We chose the fields of genetics, oncology, radiology, and the recent COVID-19 pandemic as the most representative fields addressing the cross-compliance of AI and precision medicine based on the presumption that those fields comprise the most advanced realm of AI. The following descriptive search commands were used: “artificial intelligence” AND “precision medicine” AND “genetics” (582 entries) OR “oncology” (586 entries) OR “radiology” (326 entries) OR “COVID-19” (43 entries) OR “orthopedics” 35 (entries). In all, the search retrieved 1572 relevant articles. Additionally, we used a Boolean combination of “artificial intelligence” with other medical fields such as surgery, internal medicine, cardiology, or pulmonology, which resulted in another 700 articles. However, those articles covered a wide range of overlapping subfields that could not be reasonably differentiated from one another and were therefore discarded from further analysis. English language articles were included in the search. All the articles were perused, and 76 full-text articles considered the most

Artificial Intelligence and Precision Medicine: A Perspective

representative were selected for the final evaluation. The selection was based on the weight of results, topicality of the issue, and the presence of cross-compliance between AI and precision medicine.

3

Genetics, Omics, and Precision Medicine

Genetics is a field of medicine where interconnections are expressed by mathematical equations and thus may be subject to computational analysis. As human genetics is a complex issue, and the inheritance of traits is affected by multiple genes resulting in a definite phenotype, it is hard to create a distribution model of phenotypes in a population. According to the theory of the infinitesimal model, the contribution of each gene becomes correspondingly smaller with the increasing number of genes affecting a trait (Boyle et al. 2017). Precision medicine focuses on genetic profiling to identify patients with an increased risk for specific diseases or their variants, which has a prognostic bearing or helps manage the disorder when it erupts. Modern genetics enables the performance of genomewide associated studies and the identification of single-nucleotide polymorphisms mostly residing in the noncoding genome. Such studies are conducted using AI methods coherent with multiomics. On the other hand, such noncoding variants are not well annotated or linked to specific genes, which makes it difficult to evaluate the significance of mutations (Hamamoto et al. 2019). In 2015, a deep learning-based algorithmic network was introduced for predicting the chromatin effects of sequence alterations (DeepSEA framework) in noncoding variants (Zhou and Troyanskaya 2015). A year later, the Basenji prediction model outperformed the annotations presented by the DeepSEA (Dey et al. 2020; Kelley et al. 2018), showing we are close to gaining the ability to predict the specific cell-type gene expression. The monumental volume of data gathered by various institutions on the workflow, costeffectiveness, and quality of health care needs the adoption of precision medicine assumptions

3

to become apprehended such as machine learning (ML). Genomics is one field where the application of ML is unavoidable. Gene expression data are said to be noisy, high-dimensional, and small sample-sized. The interpretation of data is complicated and often requires high computational expenses. It is even more complicated when it comes to polygenic traits. The number of solutions proposed to analyze variants and genes affecting a trait and the polygenic risk using ML methods steadily increases (Paré et al. 2017; Abraham et al. 2016). Steps have been undertaken to create databases collecting medical information such as the All Of Us Research hub set up by the National Institutes of Health, the UK Biobank, the public NIH database (ClinVar), the Genome Aggregation Database, the Encyclopedia of DNA Elements, the Gene Expression Omnibus, the Electronic Health Records, and systems integrated with other databases such as the Integrated Personal Omics Profile or the eMERGE Network. These databases share, evaluate, and process data into clinically important information (Zhang et al. 2019; Diao et al. 2018; Mitchell 2010). Precision medicine provides tools for the prediction of a course of a disease, its management, and effective treatment on many biological levels (Ahmed 2020). Taken together, genomics focuses on describing the DNA sequence, regulation of gene expression, connections between genes and diseases, and finding drugs compatible with the genome. It turns out that combining various methods is time-efficient and reduces costs. That is why hybrid methods have been proposed (Gao et al. 2017). A similar situation concerns other “omics” fields. For instance, the implementation of ML in transcriptomics improves the evaluation of RNA sequences and helps unravel the potential binding motifs, predicts new ones, and defines relations between RNA and disease or targeted drug therapy. Likewise, proteomics focuses on defining protein structure and function, protein classification and interaction, intracellular protein distribution, and finding new drugs (Zhang et al. 2019). Cytomic and proteomic techniques allow characterization of immune cell phenotypes and detect antibody-viral interactions as well as specific

4

J. Lorkowski et al.

antigens (Paczesny 2018). These last applications are most important in the recent COVID-19 pandemic (Tárnok 2020). Molecular medicine, rapidly developing along with the sequencing of the human genome, points to an enormous complexity of the human body, its diseases and treatments, and the interconnection between genetic and risk factors. The same risk factors may underlie different diseases in different subjects. The expectation placed in precision medicine has been to extend a healthy lifespan by overcoming such issues owing to the ability of tailoring to patient’s characteristics. The AI with its processing power raises the chance of performing this seemingly insurmountable task standing before precision medicine.

4

Oncology

It is of highest importance to develop techniques enabling a successful fight against cancer. Practicing precision medicine, in this case, is more difficult as it means understanding patientdisease-specific variations in complex and not fully uncovered cancer biology. Oncology uses all sources and methods of the multi-omic genre as single datasets do not provide sufficient information in this case (Chakraborty et al. 2018). Nevertheless, integration of data remains challenging. Selecting an appropriate control group requires considering personal diversity and tumor heterogeneity which influence results (Das et al. 2020). The integration of all information is a necessity as simultaneous pathologies cannot be treated separately (de Anda-Jáuregui and Hernández-Lemus 2020). ML helps categorize the type of cancer, find specific biomarkers associated with outcomes and treatment responses, select drugs, define treatment sensitivity and target drugs, predict substrates of cancer resistance, and discover driver and passenger mutations and gene-gene interactions (Nicora et al. 2020; Huang et al. 2018). Prevention and prediction of cancer are supported by AI solutions as well (Cammarota et al. 2020).

Gene mutations figure high in tumor pathogenesis. Affecting the DNA pathway of repair genes significantly increases the mutation frequency resulting in a higher predisposition for some cancer types. For instance, somatic mutations in TP53 gene are associated with a higher risk of acute myeloid leukemia, breast adenocarcinoma, bladder urothelial carcinoma, head and neck or lung squamous cell carcinoma, lung adenocarcinoma, and endometrial uterine carcinoma. Genome integrity and genes categorized as histone modifiers are associated with numerous cancer types. On the other hand, genes associated with one cancer type are classified as transcription factor/regulator, TGF-ß, and Wnt/ß-catenin signaling. These observations are conducive to discovering new therapeutic options. It is estimated that 30–94% of patients harbor actionable mutations. Moreover, the analysis of genes enables cancer allocation to a higher or lower survival rate (Kandoth et al. 2013; Berdasco and Esteller 2010). Surprisingly, patients with metastases may have acquired drug resistance following targeted treatment, the mechanism of which is not fully understood (Berger and Mardis 2018). Tumor biology remains a complex pathology interacting with the whole of the host’s organism. The microbiome, a heady subject of the current research, appears a highly underestimated player in the predisposition for developing tumors (Elinav et al. 2019; Mallick et al. 2017). Gut microbiota can affect the natural history of malignancies and influence the therapy (Cammarota et al. 2020).

5

Radiology and Radiogenomics

Radiomics is part of precision medicine extracting clinically meaningful features from medical images. Although it is of outstanding value in oncology, it can be applied in a vast majority of medical disciplines. A computeraided image analysis consists of picture segmentation, feature extraction, selection of relevant features, modeling, and validation. Initially, the imaging systems were developed to improve the diagnosis accuracy by introducing a “second

Artificial Intelligence and Precision Medicine: A Perspective

opinion”. The machine-learning technique enabled the creation of advanced software helping radiologists in everyday practice, improving cost-effectiveness and speed, and meaningfully influencing the diagnostic process (Liu et al. 2019; Santos et al. 2019). Besides defining the tumor stage, features extracted from images help determine the histopathologic type of cancer, the response to therapy, and the prognosis. Additionally, AI techniques may be implemented to supplement radiomic features with genomic data to establish more precise radiogenomics (Aktolun 2019). The integration of such data enables the holistic practice of precision medicine. For instance, tumors that are heterogeneous and require a comprehensive assessment of not only biological and molecular features but also a local expansion and potential operability would maximally benefit from such an approach. Likewise, in patients with central nervous system neoplasms, like glioma or retinoblastoma, a biopsy is often unfeasible, which makes radiogenomics of particular importance. Aerts et al. (2014) have pioneered the analysis of a tumor phenotype using noninvasive imaging and combined it with genomic data. Other studies describe connections between image features and particular mutations in lung carcinoma (Gevaert et al. 2017; Bakr et al. 2018), glioma (Seow et al. 2018), breast cancer (Pinker et al. 2018), prostate cancer (Fischer et al. 2019), hepatocellular carcinoma (Wakabayashi et al. 2019), cholangiocarcinoma, metastatic liver tumors (Saini et al. 2018), colorectal cancer, and other cancers (Horvat et al. 2019; Jansen et al. 2018). Radiogenic diversity influences nuclear medicine as well. Radiosensitivity is an individual feature. The concept of a personalized dose and personalized dose index has been proposed to avoid complications of inappropriate radiodosing (Fukunaga et al. 2019). Moreover, genome-wide association studies (GWAS) have been performed to identify phenotypes associated with an increased probability of radiationassociated adverse effects, for instance, contralateral breast cancer after breast radiation (Benitez and Knox 2020; Lee et al. 2020).

6

5

Artificial Intelligence, Precision Medicine, and Recent COVID-19 Pandemic

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing coronavirus disease known as COVID-19 pandemic, invades human cells using angiotensinconverting enzyme 2 (ACE2) receptors most richly expressed in the lung. COVID-2-associated pneumonia may run a deadly course in immunocompromised individuals or those with multiple morbidities. In some cases, changes in computer tomography (CT) scans outrun positive results of polymerase chain reaction (PCR) tests due likely to the disease incubation time (Xie et al. 2020). Also, asymptomatic patients may have lung lesions on imaging. Ground-glass opacities, interlobular septal thickening, or intralobular lines assuming a “crazy paving” pattern, and consolidations are the most specific CT changes observed in COVID-19 patients. Other less frequent changes consist of bronchiectasis, bronchial wall thickening reported in about 20% of patients, pleural thickening (32% of patients), pleural effusion (5% of patients), subpleural curvilinear lines (20% of patients), fibrosis or fibrous stripes (20% of patients), vascular enlargements, air bubble signs, nodules, halo signs, reversed halo signs (also called atoll signs), lymphadenopathy, and pericardial effusion (Ye et al. 2020). Given the advances in radiomics, the evaluation of CT scans in COVID-19 patients might be a valuable tool to distinguish COVID-19 pneumonia from that of a different origin (Wang et al. 2020; Wu et al. 2020a) or other clinical conditions (Gifani et al. 2021; Guiot et al. 2020). A deep learning model developed by Li et al. (2020) has a 90% sensitivity and 96% specificity, with the area under the ROC curve (AUC) of 0.95 and 0.96 for community-acquired pneumonia and COVID-19-related pneumonia, respectively. Wang et al. (2021) have found a 97% sensitivity and 92% specificity for the differentiation between the two types of pneumonia. Other authors have proposed the integration of radiological findings with probabilities presented

6

by AI modeling. Bai et al. (2020) have shown a higher average accuracy of 90%, sensitivity of 88%, and specificity of 91% when using the integrative method compared with 85%, 79%, and 88%, respectively, for the radiological evaluation alone. Similar solutions have been applied for X-ray diagnostics (Borkowski et al. 2020; Brunese et al. 2020; Rahaman et al. 2020). Nonetheless, differentiation of COVID-19 pneumonia from another viral pneumonia is difficult due to the likeness of imaging characteristics. Thus, volume computer-assisted reading (VCAR) thoracic software has been developed to quantitate the amount of affected lung tissue (Belfiore et al. 2020) and another software for a comprehensive distinguishing between COVID-19 and influenza pneumonia (Sotoudeh et al. 2020). The development of new pharmaceuticals is a long and strictly regulated industrial process. Computer-aided tools have also been designed for drug repurposing in COVID-19 (Mohamed et al. 2021; Ojha et al. 2021; Mohanty et al. 2020). Learning prediction models efficiently screen drugs with potential for cure of SARSCoV-2 infection. The recent pandemic situation remains a challenge for the good governance of public, social, and medical resources. Heterogeneity in presentation and outcomes in COVID-19 infection plays an important role. AI-based assistive tools have been presented in the context of free data sharing, creating free-of-charge databases, accelerating the diagnosis, minimizing the spread of the disease, stratifying patients to various risk groups, and tailoring individualized treatment (Pereira et al. 2021; Wu et al. 2020b). The COVID-19 Open Research Dataset is one of the most popular databases (Kricka et al. 2020). Interestingly, a model based on ML, consisting of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), serum ferritin, and interleukin-10 (IL-10), has been created for death risk prediction in SARS-CoV-2 infection (Guan et al. 2021). The role of AI-related discovery tools for predictive medicine becomes readily noticeable in cases of newly erupting diseases or infections. COVID-19 is an exemplary case to that end, where link prediction algorithms of AI are conducive to the process of drug repurposing

J. Lorkowski et al.

or searching for new therapeutic tools to improve curability and prognosis (Zhou et al. 2020).

7

Sensitivity of Genomic Data Concerning Public Health

The problem of dealing with sensitive data persists in the field of genomics. ML, a branch of AI particularly popular in medicine, is here used to define traits and assess the potential risk of disease. The problem concerns collecting, distributing, and sharing data between institutions. The AI also is extensively used to create large datasets of electronic health records assessing the risk of hospital readmission, mortality, or prolonged stay, and in oncology, pharmacogenomics, or community health (Lorkowski et al. 2021a; Kim et al. 2019). This predictive modeling transforms “raw” data into clinically meaningful information which improves personalized medicine and healthcare quality (Si et al. 2020; Diao et al. 2018; Rajkomar et al. 2018). Given the fact that the genomic, electronic health records and streaming data are considered the riskiest sensitivity nature of all (Beskow et al. 2020), their use outside the healthcare delivery has raised many ethical, legal, and social implications (ELSI) (Halverson et al. 2020; Tai 2020; Miller and Pickering 2011). It has been suggested that genetic exceptionalism is partly responsible for the inaccuracy of the consent process. One of the proposed solutions to this problem is a broad consent model stemming from the fact that all potential uses of data have not yet been recognized (Hemingway et al. 2018). It also is suggested to build a researcher-participant partnership to help influence the consent and research processes, decrease anxiety, and increase the patient’s engagement (Childerhose et al. 2019). Likewise, the inclusion of ordered social and behavioral data might help avoid biases, raising hope the existing injustice in the healthcare system would not be enhanced (Hollister and Bonham 2018). However, Sabatello and Juengst (2019) suggest that the “gene-hype” has been replaced by the “ELSI-hype”. Nonetheless, there

Artificial Intelligence and Precision Medicine: A Perspective

is no doubt that data integration significantly improves and enriches research outcomes. Referring to the recent attempts to find the optimal treatment for COVID-19 infections, the integration of databases on COVID-19 and neurological patients treated with the potentially antiviral amantadine seems reasonable.

8

Summary and Conclusions

AI intelligence can be divided into strong (general-purpose AI) and weak (specialized AI). Machine learning (ML), a branch of AI based on the idea that systems can substitute for humans in decision making by learning from data, uses algorithms such as the decision tree, support vector machine, Bayesian network, and deep learning methods (DL). DL builds artificial neuronal networks and focuses on creating networks reflecting the human brain consisting of multiple interconnected layers. ML is under supervision as its algorithms need data for learning, but DL performs mostly unsupervised learning models as patterns and correlations are extracted from unlabeled data. There is also reinforcement learning that makes decisions with maximum accuracy (Koteluk et al. 2021; Hamamoto et al. 2019). “Omic” data should be carefully modeled to create faultless patterns for further clinical evaluation and ensure timely personalized care (Ahmed 2020). AI solutions have several limitations such as the black box phenomenon, demand for computational power, highthroughput and large datasets, the necessity for the experience of the end-user, and overfitting. Yang et al. (2020) have presented a method of diagnosing malaria with a smartphone application using a DL algorithm. This is an example of novel practical possibilities of AI application in medical management. Likewise, the role of the finite element method is observing and simulating biomechanical phenomena by implementing AI solutions (Lorkowski et al. 2021b; Phellan et al. 2021). There seems to be a global excess of data scattered between various sources. It is

7

impossible to draw fully objective conclusions from petabytes of disconnected data. This phenomenon, called e-hoarding, is characterized by an unstoppable acquisition of data and the unwillingness to discard possessions, no matter their value (Thorpe et al. 2019). The currently available mobile computer technology, using AI and Internet of Things (IoT) solutions, has been conducive to the advent of the Fourth Industrial Revolution. Previous industrial revolutions took place over extended time intervals. Given that the present one is happening just 50 years after the Computer Revolution, a presumption arises this one could be the fastest of all if only man’s intellectual attitude and approach would be flexible enough to adjust accordingly. COVID-19 pandemic is a point-in-case test of man’s capability to respond to the moment of the moment in this context (Javaid et al. 2020). The application of AI solutions in health care, i.e., the implementation of Care 4.0, is discussable (Chute and French 2019). Proposed terms “new hands” (robots, mini-laboratories, wearable devices, customized materials, 3D printing, and speed and minimization) and “new brain” (precision medicine, AI, big data, IoT, telemedicine, and shared decision making) seem the most appropriate (Chen et al. 2019). Nonetheless, Revolution 4.0 has brought up ethical issues concerning copyrights and intellectual property rights, which have yet to be resolved (Tai 2020). Quantum mechanics has changed physics irrevocably, fractals and non-Euclidean geometry have changed mathematics, and contemporary AI and Revolution 4.0 are posed to change medicine and health care, particularly diagnostics, treatment, and pharmacoeconomics. Medical fields taking a lead in this breakthrough process are genetics, oncology, and radiology. Others like orthopedics or cardiology are following suit in driving forward precision medicine. Conflicts of Interest The authors declare no conflicts of interest concerning this article. Ethical Approval This review article does not contain any studies with human participants or animals performed by any of the authors.

8

References Abraham G, Havulinna AS, Bhalala OG, Byars SG, De Livera AM, Yetukuri L, Tikkanen E, Perola M, Schunkert H, Sijbrands EJ, Palotie A, Samani NJ, Salomaa V, Ripatti S, Inouye M (2016) Genomic prediction of coronary heart disease. Eur Heart J 37 (43):3267–3278 Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006 Ahmed Z (2020) Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum Genomics 14(1):35 Aktolun C (2019) Artificial intelligence and radiomics in nuclear medicine: potentials and challenges. Eur J Nucl Med Mol Imaging 46(13):2731–2273 Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574 (7779):505–510 Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, Tran TML, Choi JW, Wang DC, Shi LB, Mei J, Jiang XL, Pan I, Zeng QH, Hu PF, Li YH, Fu FX, Huang RY, Sebro R, Yu QZ, Atalay MK, Liao WH (2020) Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 296(3): E156–E165 Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, Zheng H, Benson JA, Zhang W, Leung ANC, Kadoch M, Hoang CD, Shrager J, Quon A, Rubin DL, Plevritis SK, Napel S (2018) A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:180202 Bauer FL (2006) Decrypted secrets: methods and maxims of cryptology, 4th edn. Springer Science+Business Media, Berlin/Heidelberg, p 111 Belfiore MP, Urraro F, Grassi R, Giacobbe G, Patelli G, Cappabianca S, Reginelli A (2020) Artificial intelligence to codify lung CT in COVID-19 patients. Radiol Med 125(5):500–504 Benitez CM, Knox SJ (2020) Harnessing genome-wide association studies to minimize adverse radiationinduced side effects. Radiat Oncol J 38(4):226–235 Berdasco M, Esteller M (2010) Aberrant epigenetic landscape in cancer: how cellular identity goes awry. Dev Cell 19(5):698–711 Berger MF, Mardis ER (2018) The emerging clinical relevance of genomics in cancer medicine. Nat Rev Clin Oncol 15(6):353–365 Beskow LM, Hammack-Aviran CM, Brelsford KM (2020) Thought leader comparisons of risks in precision medicine research. Ethics Hum Res 42(6):35–40

J. Lorkowski et al. Borkowski AA, Viswanadhan NA, Thomas LB, Guzman RD, Deland LA, Mastorides SM (2020) Using artificial intelligence for COVID-19 chest X-ray diagnosis. Fed Pract 37(9):398–404 Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169(7):1177–1186 Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Prog Biomed 196:105608 Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G (2020) Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 17(10):635–648 Chakraborty S, Hosen MI, Ahmed M, Shekhar HU (2018) Onco-multi-OMICS approach: a new frontier in cancer research. Biomed Res Int 2018:9836256 Chen C, Loh EW, Kuo KN, Tam KW (2019) The times they are a-Changin’ – healthcare 4.0 is coming! J Med Syst 44(2):40 Childerhose JE, Finnila CR, Yu JH, Koenig BA, McEwen J, Berg SL, Wilfond BS, Appelbaum PS, Brothers KB (2019) Participant engagement in translational genomics research: respect for persons-and then some. Ethics Hum Res 41(5):2–15 Chute C, French T (2019) Introducing care 4.0: an integrated care paradigm built on industry 4.0 capabilities. Int J Environ Res Public Health 16 (12):2247 Das T, Andrieux G, Ahmed M, Chakraborty S (2020) Integration of online omics-data resources for cancer research. Front Genet 11:578345 de Anda-Jáuregui G, Hernández-Lemus E (2020) Computational oncology in the multi-omics era: state of the art. Front Oncol 10:423 Dey KK, van de Geijn B, Kim SS, Hormozdiari F, Kelley DR, Price AL (2020) Evaluating the informativeness of deep learning annotations for human complex diseases. Nat Commun 11(1):4703 Diao JA, Kohane IS, Manrai AK (2018) Biomedical informatics and machine learning for clinical genomics. Hum Mol Genet 27(R1):R29–R34 Elinav E, Garrett WS, Trinchieri G, Wargo J (2019) The cancer microbiome. Nat Rev Cancer 19(7):371–376 Ergen M (2019) What is artificial intelligence? Technical considerations and future perception. Anatol J Cardiol 22(Suppl 2):5–7 Fischer S, Tahoun M, Klaan B, Thierfelder KM, Weber MA, Krause BJ, Hakenberg O, Fuellen G, Hamed M (2019) A radiogenomic approach for decoding molecular mechanisms underlying tumor progression in prostate cancer. Cancers (Basel) 11(9):1293 Freeth T, Bitsakis Y, Moussas X, Seiradakis JH, Tselikas A, Mangou H, Zafeiropoulou M, Hadland R, Bate D, Ramsey A, Allen M, Crawley A, Hockley P, Malzbender T, Gelb D, Ambrisco W, Edmunds MG (2006) Decoding the ancient Greek astronomical

Artificial Intelligence and Precision Medicine: A Perspective calculator known as the Antikythera Mechanism. Nature 444(7119):587–591 Fukunaga H, Yokoya A, Taki Y, Butterworth KT, Prise KM (2019) Precision radiotherapy and radiation risk assessment: how do we overcome radiogenomic diversity? Tohoku J Exp Med 247(4):223–235 Gao L, Ye M, Lu X, Huang D (2017) Hybrid method based on information gain and support vector machine for gene selection in cancer classification. Genomics Proteomics Bioinformatics 15(6):389–395 Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, Berry GJ, Guo HH, Lau C, Plevritis SK, Rubin DL, Napel S, Leung AN (2017) Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep 7:41674 Gifani P, Shalbaf A, Vafaeezadeh M (2021) Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans. Int J Comput Assist Radiol Surg 16(1):115–123 Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, Peng J, Guo H, Lu Y (2021) Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med 53 (1):257–266 Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Thys M, Henket M, Canivet G, Mathieu S, Eftaxia E, Lambin P, Tsoutzidis N, Miraglio B, Walsh S, Moutschen M, Louis R, Meunier P, Vos W, Leijenaar RTH, Lovinfosse P (2020) Development and validation of an automated radiomic CT signature for detecting COVID-19. Diagnostics (Basel) 11(1):41 Halverson CME, Bland ST, Leppig KA, Marasa M, Myers M, Rasouly HM, Wynn J, Clayton EW (2020) Ethical conflicts in translational genetic research: lessons learned from the eMERGE-III experience. Genet Med 22(10):1667–1672 Hamamoto R, Komatsu M, Takasawa K, Asada K, Kaneko S (2019) Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomol Ther 10(1):62 Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69S:S36–S40 Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, van Thiel GJM, Cronin M, Brobert G, Vardas P, Anker SD, Grobbee DE, Denaxas S, Innovative Medicines Initiative 2nd programme, Big Data for Better Outcomes, BigData@Heart Consortium of 20 academic and industry partners including ESC (2018) Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 39(16):1481–1495 Hollister B, Bonham VL (2018) Should electronic health record-derived social and behavioral data be used in precision medicine research? AMA J Ethics 20(9): E873–E880

9

Horvat N, Veeraraghavan H, Pelossof RA, Fernandes MC, Arora A, Khan M, Marco M, Cheng CT, Gonen M, Golia Pernicka JS, Gollub MJ, Garcia-Aguillar J, Petkovska I (2019) Radiogenomics of rectal adenocarcinoma in the era of precision medicine: a pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations. Eur J Radiol 113:174–181 Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15(1):41–51 Jansen JTM, Shrimpton PC, Holroyd J, Edyvean S (2018) Selection of bone dosimetry models for application in Monte Carlo simulations to provide CT scannerspecific organ dose coefficients. Phys Med Biol 63 (12):125015 Javaid M, Haleem A, Vaishya R, Bahl S, Suman R, Vaish A (2020) Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes Metab Syndr 14(4):419–422 Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, Leiserson MDM, Miller CA, Welch JS, Walter MJ, Wendl MC, Ley TJ, Wilson RK, Raphael BJ, Ding L (2013) Mutational landscape and significance across 12 major cancer types. Nature 502(7471):333–339 Kelley DR, Reshef YA, Bileschi M, Belanger D, McLean CY, Snoek J (2018) Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res 28(5):739–750 Kim E, Rubinstein SM, Nead KT, Wojcieszynski AP, Gabriel PE, Warner JL (2019) The evolving use of electronic health records (EHR) for research. Semin Radiat Oncol 29(4):354–361 Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A (2021) How do machines learn? Artificial intelligence as a new era in medicine. J Perinat Med 11(1):32 Kricka LJ, Polevikov S, Park JY, Fortina P, Bernardini S, Satchkov D, Kolesov V, Grishkov M (2020) Artificial intelligence-powered search tools and resources in the fight against COVID-19. EJIFCC 31(2):106–116 Lee S, Liang X, Woods M, Reiner AS, Concannon P, Bernstein L, Lynch CF, Boice JD, Deasy JO, Bernstein JL, Oh JH (2020) Machine learning on genome-wide association studies to predict the risk of radiationassociated contralateral breast cancer in the WECARE Study. PLoS One 15(2):e0226157 Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J (2020) Using artificial intelligence to detect COVID-19 and communityacquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296(2): E65–E71 Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J (2019) The applications of radiomics in precision diagnosis and treatment of

10 oncology: opportunities and challenges. Theranostics 9 (5):1303–1322 Lorkowski J, Grzegorowska O, Pokorski M (2021a) Artificial intelligence in the healthcare system: an overview. Adv Exp Med Biol. https://doi.org/10.1007/ 5584_2021_620. (Ahead of print) Lorkowski J, Wilk R, Pokorski M (2021b) In silico evaluation of treatment of periprosthetic fractures in elderly patients after hip arthroplasty. Adv Exp Med Biol 1289:115–123 Mallick H, Ma S, Franzosa EA, Vatanen T, Morgan XC, Huttenhower C (2017) Experimental design and quantitative analysis of microbial community multiomics. Genome Biol 18(1):228 Miller ST, Pickering RG (2011) Use of electronic patient data in research. AMA J Ethics 13:148–151 Mitchell R (2010) National biobanks: clinical labor, risk production, and the creation of biovalue. Sci Technol Hum Values 35(3):330–355 Mohamed K, Yazdanpanah N, Saghazadeh A, Rezaei N (2021) Computational drug discovery and repurposing for the treatment of COVID-19: a systematic review. Bioorg Chem 106:104490 Mohanty S, Harun Ai Rashid M, Mridul M, Mohanty C, Swayamsiddha S (2020) Application of artificial intelligence in COVID-19 drug repurposing. Diabetes Metab Syndr 14(5):1027–1031 Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R (2020) Integrated multi-omics analyses in oncology: a review of machine learning methods and tools. Front Oncol 10:1030 Ojha PK, Kar S, Krishna JG, Roy K, Leszczynski J (2021) Therapeutics for COVID-19: from computation to practices - where we are, where we are heading to. Mol Divers 25(1):625–659 Paczesny S (2018) Biomarkers for posttransplantation outcomes. Blood 131(20):2193–2204 Pakzad A, Iacoviello F, Ramsey A, Speller R, Griffiths J, Freeth T, Gibson A (2018) Improved X-ray computed tomography reconstruction of the largest fragment of the Antikythera Mechanism, an ancient Greek astronomical calculator. PLoS One 13(11):e0207430 Paré G, Mao S, Deng WQ (2017) A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci Rep 7(1):12665 Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, Tuteja S, Cresci S (2021) COVID-19: understanding inter-individual variability and implications for precision medicine. Mayo Clin Proc 96(2):446–463 Phellan R, Hachem B, Clin J, Mac-Thiong JM, Duong L (2021) Real-time biomechanics using the finite element method and machine learning: review and perspective. Med Phys 48(1):7–18 Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 287(3):732–747

J. Lorkowski et al. Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, Qi S, Kong F, Zhu X, Zhao X (2020) Identification of COVID-19 samples from chest X-Ray images using deep learning: a comparison of transfer learning approaches. J Xray Sci Technol 28 (5):821–839 Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med 1:18 Rankin JL (2018) A People’s history of computing in the United States. Harvard University Press, Cambridge. https://doi.org/10.4159/9780674988538 Sabatello M, Juengst E (2019) Genomic essentialism: its provenance and trajectory as an anticipatory ethical concern. Hast Cent Rep 49(Suppl 1):S10–S18 Saini A, Breen I, Pershad Y, Naidu S, Knuttinen MG, Alzubaidi S, Sheth R, Albadawi H, Kuo M, Oklu R (2018) Radiogenomics and radiomics in liver cancers. Diagnostics (Basel) 9(1):4 Santos MK, Ferreira Júnior JR, Wada DT, Tenório APM, Barbosa MHN, Marques PMA (2019) Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiol Bras 52(6):387–396 Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N (2018) Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 91(1092):20170930 Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, Jim Zheng W, Roberts K (2020) Deep representation learning of patient data from Electronic Health Records (EHR): a systematic review. J Biomed Inform 31:103671 Sotoudeh H, Tabatabaei M, Tasorian B, Tavakol K, Sotoudeh E, Moini AL (2020) Artificial intelligence empowers radiologists to differentiate pneumonia induced by COVID-19 versus influenza viruses. Acta Inform Med 28(3):190–195 Tai MC (2020) The impact of artificial intelligence on human society and bioethics. Tzu Chi Med J 32 (4):339–343 Tárnok A (2020) Machine learning, COVID-19 (2019nCoV), and multi-OMICS. Cytometry A 97 (3):215–216 Thorpe S, Bolster A, Neave N (2019) Exploring aspects of the cognitive behavioural model of physical hoarding in relation to digital hoarding behaviours. Digit Health 5:2055207619882172 Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, Felli E, Saviano A, Agnus V, Savadjiev P, Baumert TF, Pessaux P, Marescaux J, Gallix B (2019) Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int 13(5):546–559

Artificial Intelligence and Precision Medicine: A Perspective Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, Wang M, Qiu X, Li H, Yu H, Gong W, Bai Y, Li L, Zhu Y, Wang L, Tian J (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56(2):2000775 Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, Zheng Z, Sun W, Lan L, Zhang W, Mu X, Shi C, Wang Z, Lee J, Jin Z, Lin M, Jin H, Zhang L, Guo J, Zhao B, Ren Z, Wang S, Xu W, Wang X, Wang J, You Z, Dong J (2021) AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system. Appl Soft Comput 98:106897 Wu X, Hui H, Niu M, Li L, Wang L, He B, Yang X, Li L, Li H, Tian J, Zha Y (2020a) Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur J Radiol 128:109041 Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, Frix AN, Louis R, Moutschen M, Li J, Li J, Yan C, Du D, Zhao S, Ding Y, Liu B, Sun W, Albarello F, D’Abramo A, Schininà V, Nicastri E, Occhipinti M, Barisione G, Barisione E, Halilaj I, Lovinfosse P, Wang X, Wu J, Lambin P (2020b) Development of a clinical decision support system for severity risk

11

prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J 56(2):2001104 Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020) Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology 296(2):E41–E45 Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S (2020) Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inform 24 (5):1427–1438 Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol 30 (8):4381–4389 Zhang Z, Zhao Y, Liao X, Shi W, Li K, Zou Q, Peng S (2019) Deep learning in omics: a survey and guideline. Brief Funct Genomics 18(1):41–57 Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931–934 Zhou Y, Wang F, Tang J, Nussinov R, Cheng F (2020) Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health 2(12):e667–e676

Adv Exp Med Biol - Clinical and Experimental Biomedicine (2022) 15: 13–22 https://doi.org/10.1007/5584_2021_636 # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Published online: 30 April 2021

Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge Jacek Lorkowski

and Karolina Gawronska

Abstract

Pedobarography is a modern technology enabling the assessment of the locomotor system based on the plantar pressure distribution. The technic is useful in the rehabilitation of various types of dysfunction of body movement. This chapter aims to describe the application of pedobarography in clinical therapy. The qualitative analysis is based on a review of articles in English, French, German, Polish, Portuguese, Spanish, Turkish, and Chinese in Medline/PubMed, Cochrane Library, Embase, and PEDro databases. The search covered the articles on clinical trials, randomized controlled trials, meta-analyses, and reviews published over 1984–2020. The literature shows that pedobarography is a safe non-invasive method that is useful for the examination of foot biomechanics with a reference to the entire musculoskeletal system. A pedobarographic examination enables insight J. Lorkowski (*) Clinical Department of Orthopedics, Traumatology and Sports of the Central Clinical Hospital of the Ministry of the Internal Affairs and Administration, Warsaw, Poland Faculty of Health Sciences, Medical University of Mazovia, Warsaw, Poland e-mail: [email protected] K. Gawronska Rehabilitation Centre of the Central Clinical Hospital of the Ministry of the Internal Affairs and Administration, Warsaw, Poland e-mail: [email protected]

into a motion disorder, its plausible relation to a systemic pathology, and monitoring the course of treatment and rehabilitation. Keywords

Foot · Gait · Pedobarography · Physiotherapy · Plantar pressure · Rehabilitation · Underfoot pressure

1

Background

The pedobarographic examination is a modern non-invasive diagnostic method of the locomotor system based on the plantar pressure distribution. It provides a graphical representation of the possible asymmetry of the pressure distribution, pathological overloads, lack of pressure, or “functional amputation” of a fragment of the foot (Telfer and Bigham 2019; Vette et al. 2019). Combined with the clinical and radiological signs, it helps diagnose pathologies of lower limbs’ joints, providing insights into the function of the entire locomotor system (Lorkowski and Zarzycki 2006). There are three types of pedobarographic examinations: static, postural, and dynamic. The primary and simplest is static pedobarography, which describes plantar pressure distribution during a specified time of standing. The postural examination considers the manner and poses characteristic of one’s stance. Dynamic pedobarography, capturing the foot 13

14

J. Lorkowski and K. Gawronska

propulsion phase, deals with the load acting on the plantar surface of the foot during gait (Hagen et al. 2020; Güven et al. 2016). To perform a full analysis of the pressure distribution on the plantar surface, it is advisable to use one of the existing detailed classifications of foot division (Kernozek et al. 1996; Blomgren et al. 1991; Cavanagh et al. 1987). Despite the availability of modern pedobarography and increasing knowledge on the role of foot health in the overall functioning and quality of life, a proportion of medical reports on the matter have a negligible share of 0.066% in the fields of orthopedics, traumatology, and rehabilitation. The first article on pedobarography we identified in databases came from 1984, while the examination itself had been in use as of 1947 (Lorkowski et al. 2015). In the Medline/PubMed database, pedobarography is a subject of only 299 articles over the last five years. In this review, we set out to examine medical databases in search of articles dealing with the applicability of pedobarographic examination in diagnosis, clinical course, and physiotherapy of foot ailments.

2

Database Search

We searched Medline/PubMed, Cochrane Library, Embase, and PEDro databases for articles on pedobarography in physical therapy published in English, French, German, Polish, Portuguese, Spanish, Turkish, and Chinese. The search encompassed clinical trials, randomized controlled trials, metaanalyses, reviews, and systematic reviews. A string search using the Boolean operators “AND” and “OR” was used covering the years 1984–2020. The following commands were used: “Physical therapy” or “Rehabilitation” AND (“Pedobarography” OR “Plantar pressure distribution” OR “Foot pressure distribution” OR “Underfoot pressure distribution” OR “Podoscopy” OR “Baropodometry”). Additionally, a hand search using the keywords “Pedobarography”, “Plantar pressure distribution”, “Foot pressure distribution”, “Underfoot pressure distribution”, “Podoscopy”, and “Baropodometry” was performed in the Google Scholar engine.

3

Search Results

The adopted scheme and results of the article selection are depicted in Fig. 1. A total of 588 articles were identified. All published articles were reviewed and relevant studies were selected. The articles that explicitly focused on treatment or medical diagnosis without a reference to physical therapy as well as case reports, books, and documents were excluded. After exclusions, 202 articles covering a large thematical range of current applicability of pedobarography were retained for analysis. Seven main themes emerged (Table 1). After the examinations of contents, 57 of these articles were chosen as the most significant and innovative information to be discussed in this review. Studies reveal that the pedobarography of plantar pressure distribution during both stance and gait is useful for the biomechanical analysis of foot, lower limb, and axial skeleton in clinical practice. The orthopedic category contains articles referring to physiotherapy in the course of diseases and injuries of the musculoskeletal system in both adults and children. It also includes physiotherapeutic methods and techniques such as stretching, strengthening, closed kinetic chain exercises, or taping. Another category concerns physiotherapy in neurologic disabilities due to stroke, sclerosis multiplex, Parkinson’s disease, or cerebral palsy. Diabetic foot, polyneuropathy, and osteoporosis are the main topics of articles concerning rehabilitation in diabetology. Rheumatic foot management and ankylosing spondylitis belong to another category (Rogers et al. 2020; Konings-Pijnappels et al. 2019; Yuan et al. 2019; Notarnicola et al. 2018; Cerrahoglu et al. 2016; Skopljak et al. 2014). The use of pedobarography in rehabilitation based on innovative assisting devices and virtual reality is also singled out. Pedobarographic testing is used for the functional assessment of the patient before and after the implementation of physiotherapeutic interventions, monitoring, and modification of the rehabilitation procedure (Jasiewicz et al. 2019; Molund et al. 2018). The application of pedobarography in sports and other disciplines is

Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge

15

Fig. 1 Flow of article selection

Table 1 Pedobarography application in physiotherapy Orthopedic diseases Neurology Diabetology Rheumatology New technologies in physical therapy Sport Others

rare. Highly specialized areas of physiotherapy will not be addressed in this review as such issues go beyond the competence of a clinical physiotherapist.

4

Physiotherapy in Orthopedic Diseases

Static and dynamic pedobarography is particularly useful for the biomechanical examination

Articles, n (%) 46 (23%) 38 (19%) 32 (16%) 25 (12%) 7 (3%) 32 (16%) 22 (11%)

of the foot and lower limb before and after the surgical treatment of hallux valgus. Schuh et al. (2009) have evaluated the plantar pressure distribution during the stance phase of gait in patients with hallux valgus who underwent the corrective Austin and Scarf osteotomy. They show that the maximum force and force-time integral in the first metatarsal head region and the great toe region increased after the surgery and 4–6 weeks of a multimodal rehabilitation program. These findings show that postoperative physical therapy

16

improves the function and weight-bearing of the first ray after hallux valgus osteotomy. The same group of authors has shown in another study that dynamic pedobarography evaluation of the pressure distribution on the foot sole after chevron osteotomy is conducive to effective postoperative rehabilitation, including mobilization, manual therapy, strengthening exercises, and gait training. In that study, the mean maximum force of the great toe and first metatarsal head region increases from 72.2 N/122.5 N preoperatively to 106.8 N/144.7 N one year postoperatively. The mean contact area and force-time integrals increase as well (Schuh et al. 2010). A selection of a rehabilitation program after the hallux valgus surgery is much aided by the evaluation of the pressure distribution on the foot sole, which enables the assessment of the efficiency of the first radius and other foot parts (Mickle et al. 2011). The pedobarographic examination is useful in the diagnosis and rehabilitation of the foot after the surgical treatment of Lisfranc fractures. The midfoot peak plantar pressure and a force-time integral beneath the second metatarsal show appreciable reductions six months after surgery. The Lisfranc injury changes the postural control and muscle strength of a lower limb. Thus, rehabilitation should be directed at the improvement of proprioception and calf muscular strength by the exercise of dorsal extensors (Mehlhorn et al. 2017). Postural pedobarographic examinations have also been used in patients with a trimalleolar fracture. During rehabilitative full loading of the limb, increased pressures at the calcaneus on the plantar surface of the foot and the lateral midfoot side have been found in the limb opposite to the fracture site. Moreover, an increase in the foot contact area has been found on the injured side. The foot sole pressures get normalized during physiotherapy (Lorkowski et al. 2003a). The calcaneal bone fracture is another example of the use of pedobarography for monitoring the effects of foot rehabilitation. During a six-month-long follow-up after injury, persisting decreases in the maximum pressure and foot contact area at the injured side and gradual decreases in the lateral

J. Lorkowski and K. Gawronska

forefoot, midfoot, and hindfoot regions overload on the opposite side have been found (Hagen et al. 2020). Single reports demonstrate the advantage of pedobarography in monitoring results of the surgical gastrocnemius muscle recession when compared to the standard stretching physiotherapy in patients with plantar heel pain lasting for more than one year. A pedobarographic examination confirmed the increase in forefoot plantar pressure from 536 (range of 306–708) kPa to 642 (range of 384–885) kPa in the surgery group increased compared to the standard physiotherapy, validating the surgery as a safe and effective method of treating chronic plantar heel pain (Molund et al. 2018)). Likewise, pedobarography facilitates the monitoring of rehabilitative treatment in patients after an Achilles tendon rupture (Ozkan et al. 2016). In the initial stage of rehabilitation, lower pressure values and greater maximum underfoot pressures in a bipedal position have been identified in the limb where the Achilles tendon continuity is interrupted. There is increased maximum pressure in the hindfoot and decreased beneath the heads of metatarsal bones. The pressure on the side opposite to the fracture is greater in all foot regions, especially under the heads of the 1st– 3rd metatarsal bones and in the hindfoot area. It has been noticed that during the bipedal standing on tiptoes, the pressure beneath the heads of the 1st–5th metatarsal bones is lower, and the toe is partially absent from the ground. Rehabilitation reverses the pressure level to the normal state (Lorkowski et al. 2001a, b). Pedobarography also is useful for the evaluation of lower limb biomechanics after total knee arthroplasty. Notarnicola et al. (2018) have shown that the gravity center during stance shifts to that present in the normal position. Additionally, the pressure exerted by gait on the foot of the limb operated on normalizes, which is evident in all follow-ups. Dag et al. (2019) have investigated dynamic pedobarography to assess the foot pressure distribution in young adults with patellofemoral pain syndrome. The patients were asked to walk on the treadmill for 7 min at preferred walking speeds and then at a 30% greater

Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge

speed. There was no appreciable difference in the pressure distribution between the symptomatic and non-symptomatic limbs or between the patients and control healthy subjects. The assessment of the plantar pressure distribution is useful in monitoring the rehabilitation process after the anterior cruciate ligament reconstruction. Mittlmeier et al. (1999) have performed pedobarographic tests at 6, 12, and 24 weeks after surgery during ground-level walking and descending stairs. The operated-on and non-injured legs were compared in 10 patients. A significant decrease in gait asymmetry is noticed 12 weeks after reconstruction. The authors emphasize that the knowledge on the foot pressure distribution may provide for a more personalized physiotherapy program. Lorkowski et al. (2009) have made noteworthy clinical observations of changes in the plantar pressure distribution in patients after the surgical treatment of a proximal femur fracture. A total of 22 patients, both female and male, were assessed after surgery and rehabilitation of femoral neck fracture (9 patients) and pertrochanteric fracture (13 patients). The Blomgren regional classification and static and postural examinations were used (Blomgren et al. 1991). The values of T and H region pressures decreased in 12 patients. In another six patients, a decrease in the MT1– MT5 regions was noticed, and in three, decreases in the pressure values in all regions were found. A pressure increase occurred in T and H regions in only one patient. Thus, the pedobarographic examination revealed that despite the surgical fixation of a fractured femur and physiotherapy, the normal plantar pressure distribution is not reinstated. Another practical application of pedobarographic testing is to evaluate the plantar pressure distribution in patients with coxarthrosis. Rongies et al. (2009) have evaluated the effects of a 15-day rehabilitation program and found that the balance between the average and maximum underfoot pressure gets normalized in both feet, and the area drawn by the center of pressure sway is reduced. Using pedobarography, it is possible to follow changes in the plantar pressure distribution in patients with degenerative changes in the spine

17

and thoracic-lumbar scoliosis (Lorkowski et al. 2001a, b, 2003b). Kim and Kim (2019) have investigated the distribution pattern of pressure on the plantar surface of the foot depending on the forward head position angle. There are 15 patients in the study, who are treated with the oscillatory stimulation training of the shoulder joint using a bodyblade exercise kit. Craniovertebral angle and cranial rotation angle are measured to evaluate changes in the forward head position. The pedobarography findings are that forefoot and hindfoot pressure distributions significantly improve in the bodyblade group when compared to the control group subjected to the standard physiotherapy. Thus, the oscillation training with bodyblade improves the forward head position angle and consequently the body posture. Braun et al. (2017) have used a continuously measuring pedobarography shoe insole for monitoring the patient compliance to weight-bearing recommendations after surgical corrections of the ankle, tibial shaft, and intertrochanteric femur fractures. The insole was inserted into a shoe shortly after the surgery and left in place for up to 28 days, and weight-bearing was controlled by physical therapy according to institutional standards. About one-half of the 30 patients persevered in the imposed limit of weightbearing. The other half deviated from the recommendations by over 50% within two weeks after the surgery, pointing to poor adherence to weight-bearing recommendations. The study shows a novel pedobarographic shoe insole enabling the feedback modification of the patient compliance with recommended physical therapy in real-time. Control of limb loading is essential in the assessment of rehabilitation progress. A pedobarographic examination is used to evaluate the effects of other physiotherapeutic procedures, for instance, low-dye taping. The distribution pattern of the plantar pressure in patients with excessively pronated feet reveals that low-dye taping increases the peak and mean plantar pressure under the lateral midfoot and toes and decreases under the heel and forefoot. These findings indicate less foot pronation (Lange et al. 2004). Likewise, pedobarography has

18

J. Lorkowski and K. Gawronska

confirmed the effective decrease in the peak hindfoot under pressure in patients with heel pad atrophy who were treated with the figure-8modification of low-dye taping (Chae et al. 2018). Baropodometry has been used for the assessment of kinesio-taping on the neuromuscular status of femoral quadriceps and balance in patients after an anterior cruciate ligament tear, showing no correction of the inclination of the gravity center (Oliveira et al. 2016). Single reports describe the use of pedobarography in the assessment of closed kinetic chain exercises or other physiotherapeutic techniques (Xiang et al. 2020; Lee et al. 2013). In orthopedic physiotherapy, pedobarographic examinations are performed in children to diagnose flat feet with and without overweight (Paolucci et al. 2020; Cimolin et al. 2016). The clinical assessment of flat foot is particularly difficult and unreliable in obese children where the fat tissue on the medial side of a foot, decreasing the longitudinal arch, may mislead the diagnosis. Untreated flat feet in children may lead to future irreversible structural foot damage and contribute to numerous musculoskeletal disorders. Pedobarography is essential in such cases to diagnose a defect and monitor its treatment (Lorkowski et al. 1998).

5

Physiotherapy in Neurologic Diseases

Research emphasizes the use of pedobarography in neurology. The examination of underfoot pressure distribution is used in the diagnostics of motor dysfunction in Parkinson’s disease (Giardini et al. 2018). Another example is ischemic stroke-related motor disorders. Changes in foot structure and function after a stroke, reflected in the plantar pressure distribution, affect balance and mobility (Rogers et al. 2020; Nam et al. 2017; Forghany et al. 2015). Pedobarography helps select appropriate techniques of rehabilitation and monitor the progress of rehabilitation. In patients with multiple sclerosis, it may detect a motor impairment before the symptomatic gait disorders, caused by ataxia and hypertonic

muscles, and secondary musculoskeletal system deformities affect the normal plantar pressure, which disturbs the accommodation of the foot to the ground while walking. Dynamic pedobarography detects disruption of the hindfoot loading and inappropriate timing during load transfer from the hindfoot to the forefoot in patients with multiple sclerosis (Keklicek et al. 2018). Such findings help predict the disease course and choose the rehabilitation strategy. The distribution of plantar pressure could be an excellent non-invasive assessment for the monitoring of rehabilitation treatment after spinal cord injury. Studies on walking quality conducted by Yuan et al. (2019) have demonstrated that the peak pressures under the metatarsal heads and toe are lower and the percentage of a heel contact area is greater in spinal-injured patients when compared with healthy subjects. The examination of underfoot pressure distribution is also used in the rehabilitation treatment of children with cerebral palsy. The disease is accompanied by motor, postural balance, and spasticity disorders, which lead to the equinus and gait deformity. One of the forms of rehabilitation support for children with cerebral palsy is ankle-foot orthosis. Pedobarography is the method of choice to control the application of an adjustable splint-assisted ankle-foot orthosis and twister wrap orthosis that work to normalize the plantar pressure distribution and thus restore the gait pattern and gait stability (Eid et al. 2018; Chen et al. 2017; Pauk et al. 2016).

6

Physiotherapy in Diabetes Mellitus

Pedobarography was first used in the complications accompanying diabetes. Angiopathies and neuropathies of diabetes increase the pressure on the plantar surface of the foot, most often under the head of the second and third metatarsal bones, leading to the formation of hard-to-heal ulcers and Charcot’s arthro-neuropathy. Pedobarography facilitates the selection of appropriate insoles and footwear, which are essential adjuncts to pharmacotherapy to reduce the risk of trophic changes and

Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge

the development of full-blown diabetic foot and its complications (Chatzistergos et al. 2020; Bagherzadeh Cham et al. 2018). Cerrahoglu et al. (2016) have studied the effects of exercise aimed at the increase in the range of motion, stretching, and strengthening of foot joints in diabetic patients. With the aid of static and dynamic pedobarographic examination, the authors demonstrate the normalization of plantar pressure distribution after 4 weeks of exercises. Baropodomometric testing has also been used to assess postural control in osteoporosis. Postural changes in the spine, particularly thoracic hyperkyphosis, known as Dowager’s Hump or lumbar kyphosis, lead to an increased deflection of the body’s center of gravity, which combined with a limited visual field caused by a poor global sagittal alignment contributes to falls. Baropodometer testing is of great help in the risk assessment of falls in patients with osteoporosis (Cultrera et al. 2010).

7

Pedobarography in Rheumatology

Load distribution on the plantar surface of the foot is measured to diagnose and rehabilitate the rheumatoid foot (Șerban et al. 2020; Lorkowski et al. 2015; Yoon 2015). Patients with rheumatoid arthritis show a 2–3-fold increase in pressure under the head of each metatarsal bone, flattening of the transverse arch, and a lateral maximum weight shift in the foot area (Hagen et al. 2020). Konings-Pijnappels et al. (2019) suggest that a lack of association between the local disease activity and the plantar foot pressure in rheumatoid arthritis might be due to rare swelling or pain in metatarsophalangeal joints. In the initial stages of rheumatoid arthritis, when foot deformities are minor, pedobarographic examination enables the selection of appropriate insoles or orthopedic shoes while in advanced lesions; it facilitates the planning of surgical treatment and then monitoring its results. Pedobarograhy in patients suffering from ankylosing spondylitis has yielded fewer findings. Aydin et al. (2015) have assessed the

19

effects of posture alterations on the distribution of plantar foot pressure in 38 patients with ankylosing spondylitis and 33 healthy subjects. The static mode of testing does not reveal significant differences between the two groups, whereas the dynamic mode shows changes in peak pressures under the metatarsal and midfoot regions. The percentage of the midfoot plantar contact area is greater in ankylosing spondylitis patients when compared to healthy subjects. Knowledge of the plantar pressure distribution in ankylosing spondylitis enables a more individualized approach to set the rehabilitation program.

8

New Technologies in Rehabilitation

Reports on the possible clinical use of pedobarography draw attention to innovative solutions in physical therapy. The implementation of new technologies such as inertial sensors, depth cameras, stabilometry platforms, or laser sensing in the work-up of gait disorders enables the objective prompt diagnosis and rehabilitative treatment. An example of an intelligent gaitimproving device is a robotic exoskeleton hip-assistive device based on the mechatronic system. Lee et al. (2017) have shown in elderly patients using dynamic pedobarography that the device causes a significant improvement in the foot pressure distribution, concerning the maximum force and peak pressure of the entire foot – medial, anterior, and posterior masks. Additionally, improvements in gait speed, cadence, stride length, and single support time were noticed. Noteworthy, new technologies used in the prediction and detection systems based on machine learning algorithms may help prevent falls in older age and their consequences (Gawrońska and Lorkowski 2020). Cano-Manas et al. (2017) have demonstrated using a semi-immersive virtual reality protocol in stroke patients that the pedobarographic assessment of pressure distribution on the plantar surface of the foot helps monitor the effects of physiotherapy treatment. Patients participated in

20

J. Lorkowski and K. Gawronska

an eight-week rehabilitation comprising conventional physiotherapy or its combination with occupational therapy. Consecutive sessions were extended in time and intensity, using commercial video games linked to Xbox 360 console and Kinect sensor. The findings show a superior improvement in balance and postural control when both therapies are combined as opposed to conventional physiotherapy alone.

9

Conclusions

Pedobarography is a safe, non-invasive method whose potential for advancing the diagnostic and physiotherapeutic procedures concerning the musculoskeletal system and particularly foot biomechanics is increasingly recognizable in clinical practice. A pedobarographic examination helps predict or unravel the susceptibility to falls in the aftermath of gait and motion coordination disorders most often accompanying neurodegenerative disorders and the aging process. Thus, it is not only a valuable adjunct to rehabilitative physiotherapy but may also spare orthopedic interventions and serious health and quality of life detriments, particularly in the elderly. The most recent developments combining pedobarography with technologically advanced software, like photogrammetry, are poised to become critical elements of the Industry 4.0 revolution in broad areas of medicine, such as orthopedics, rehabilitation, neurology, or sports. The revolution comprises automation and artificial intelligence to create new opportunities and innovative ways of personalized patient treatment. Conflicts of Interest The authors declare no conflicts of interest concerning this chapter. Ethical Approval This chapter does not contain any studies with human participants or animals performed by any of the authors.

References Aydin E, Turan Y, Tastaban E, Kurt Omurlu I, Sendur OF (2015) Plantar pressure distribution in patients with ankylosing spondylitis. Clin Biomech (Bristol, Avon) 30(3):238–242 Bagherzadeh Cham M, Mohseni-Bandpei MA, Bahramizadeh M, Kalbasi S, Biglarian A (2018) The effects of vibro-medical insole on sensation and plantar pressure distribution in diabetic patients with mild-tomoderate peripheral neuropathy. Clin Biomech (Bristol, Avon) 59:34–39 Blomgren M, Turan I, Agadir M (1991) Gait analysis in hallux valgus. J Foot Surg 30(1):70–71 Braun BJ, Veith NT, Rollmann M, Orth M, Fritz T, Herath SC, Holstein JH, Pohlemann T (2017) Weight-bearing recommendations after operative fracture treatmentfact or fiction? Gait results with and feasibility of a dynamic, continuous pedobarography insole. Int Orthop 41(8):1507–1512 Cano-Manas MJ, Collado-Vazquez S, Cano-de-la-Cuerda R (2017) Commercial video games in the rehabilitation of patients with sub-acute stroke: a pilot study. Rev Neurol 65(8): 337–347 (Article in Spanish) Cavanagh PR, Rodgers MM, Iiboshi A (1987) Pressure distribution under symptom-free feet during barefoot standing. Foot Ankle 7(5):262–276 Cerrahoglu L, Koşan U, Sirin TC, Ulusoy A (2016) Range of motion and plantar pressure evaluation for the effects of self-care foot exercises on diabetic patients with and without neuropathy. J Am Podiatr Med Assoc 106(3):189–200 Chae YH, Kim JS, Kang Y, Kim HY, Yi TI (2018) Clinical and biomechanical effects of low-dye taping and figure-8 modification of low-dye taping in patients with heel pad atrophy. Ann Rehabil Med 42 (2):222–228 Chatzistergos PE, Gatt A, Formosa C, Farrugia K, Chockalingam N (2020) Optimised cushioning in diabetic footwear can significantly enhance their capacity to reduce plantar pressure. Gait Posture 79:244–250 Chen W, Liu X, Pu F, Yang Y, Wang L, Liu H, Fan Y (2017) Conservative treatment for equinus deformity in children with cerebral palsy using an adjustable splint-assisted ankle-foot orthosis. Medicine (Baltimore) 96(40):e8186 Cimolin V, Capodaglio P, Cau N, Galli M, Pau M, Patrizi A, Tringali G, Sartorio A (2016) Foot-type analysis and plantar pressure differences between obese and nonobese adolescents during upright standing. Int J Rehabil Res 39(1):87–91 Cultrera P, Pratelli E, Petrai V, Postiglione M, Zambelan G, Pasquetti P (2010) Evaluation with stabilometric platform of balance disorders in

Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge osteoporosis patients. A proposal for a diagnostic protocol. Clin Cases Miner Bone Metab 7(2):123–125 Dag F, Dal U, Altinkaya Z, Erdogan AT, Ozdemir E, Yildirim DD, Colak M (2019) Alterations in energy consumption and plantar pressure distribution during walking in young adults with patellofemoral pain syndrome. Acta Orthop Traumatol Turc 53(1):50–55 Eid MA, Aly SM, Mohamed RA (2018) Effect of twister wrap orthosis on foot pressure distribution and balance in diplegic cerebral palsy. J Musculoskelet Neuronal Interact 18(4):543–550 Forghany S, Nester CJ, Tyson SF, Preece S, Jones RK (2015) Plantar pressure distribution in people with stroke and association with functional consequences. Physiotherapy 101(Suppl 1):eS399–eS400 Gawrońska K, Lorkowski J (2020) Falls, aging and public health – a literature review. Ortop Traumatol Rehabil 22(6):397–408 Giardini M, Nardone A, Godi M, Guglielmetti S, Arcolin I, Pisano F, Schieppati M (2018) Instrumental or physical-exercise rehabilitation of balance improves both balance and gait in Parkinson’s disease. Neural Plast 2018:5614242 Güven M, Kocadal O, Akman B, Poyanl{ OS, Kemah B, Atay EF (2016) Proximal femoral nail shows better concordance of gait analysis between operated and uninjured limbs compared to hemiarthroplasty in intertrochanteric femoral fractures. Injury 47 (6):1325–1331 Hagen L, Pape JP, Kostakev M, Peterlein CD (2020) Pedobarographic changes during first month after subtalar extra-articular screw arthroereisis (SESA) operation of juvenile flexible flatfoot. Arch Orthop Trauma Surg 140(3):313–320 Jasiewicz B, Klimiec E, Młotek M, Guzdek P, Duda S, Adamczyk J, Potaczek T, Piekarski J, Kołaszczyński G (2019) Quantitative analysis of foot plantar pressure during walking. Med Sci Monit 3(25):4916–4922 Keklicek H, Cetin B, Salci Y, Balkan AF, Altinkaynak U, Armutlu K (2018) Investigating the dynamic plantar pressure distribution and loading pattern in subjects with multiple sclerosis. Mult Scler Relat Disord 20:186–191 Kernozek TW, LaMott EE, Dancisak MJ (1996) Reliability of an in-shoe pressure measurement system during treadmill walking. Foot Ankle Int 17(4):204–209 Kim EK, Kim SG (2019) Forward head posture (FHP) angle and plantar pressure resulting from oscillatory stimulation training of the shoulder joint: a randomized controlled trial. J Back Musculoskelet Rehabil 32 (1):37–42 Konings-Pijnappels APM, Tenten-Diepenmaat M, Dahmen R, Verberne SK, Dekker J, Twisk JWR, Roorda LD, van der Leeden M (2019) Forefoot pathology in relation to plantar pressure distribution in patients with rheumatoid arthritis: a cross-sectional study in the Amsterdam foot cohort. Gait Posture 68:317–322

21

Lange B, Chipchase L, Evans A (2004) The effect of low-dye taping on plantar pressures, during gait, in subjects with navicular drop exceeding 10 mm. J Orthop Sports Phys Ther 34(4):201–209 Lee NK, Kwon JW, Son SM, Nam SH, Choi YW, Kim CS (2013) Changes of plantar pressure distributions following open and closed kinetic chain exercise in patients with stroke. Neuro Rehabilitation 32 (2):385–390 Lee SH, Lee HJ, Chang WH, Choi BO, Lee J, Kim J, Ryu GH, Kim YH (2017) Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults. J Neuroeng Rehabil 14(1):123 Lorkowski J, Zarzycki D (2006) Clinical use of pedobarographic examination-own experience and review of literature. Przegl Lek 63(Suppl 5):28–32 Lorkowski J, Radło W, Skawina A (1998) Flat feet in children. Emed-system investigation. Med Sci Monit 4(Suppl.2):98–99 Lorkowski J, Lorkowska B, Skawina A (2001a) Underfoot pressure distribution of patients with spinal problems. Acta Kinesiol Univ Tart 6(Suppl):152–155 Lorkowski J, Skawina A, Mazur T (2001b) Estimation of underfoot pressure distribution during treatment of patients with partial rupture of Achilles tendon. Acta Kinesiol Univ 6(Suppl):156–159 Lorkowski J, Mazur T, Skawina A (2003a) Underfoot pressure distribution in patients after ankle injury. Adv Polish Clin Anat:156–164 Lorkowski J, Mazur T, Skawina A (2003b) Pressure distribution under feet and under ischiadic tubers in patients with degenerative changes of lumbosacral part of vertebral column and low back pain. Adv Polish Clin Anat:173–180 Lorkowski J, Hładki W, Trybus M, Zarzycki D (2009) Underfoot pressure distribution in patients after surgical treatment of proximal end of femoral bone. J Orthop Trauma Surg Rel Res 3(15):28–35 Lorkowski J, Grzegorowska O, Kotela I (2015) The use of pedobarographic examination to biomechanical evaluation of foot and ankle joint in adult–own experience. Ortop Traumatol Rehabil 17(2):207–213 Mehlhorn AT, Walther M, Yilmaz T, Gunst L, Hirschmüller A, Südkamp NP, Schmal H (2017) Dynamic plantar pressure distribution, strength capacity and postural control after Lisfranc fracturedislocation. Gait Posture 52:332–337 Mickle KJ, Munro BJ, Lord SR, Menz HB, Steele JR (2011) Gait, balance and plantar pressures in older people with toe deformities. Gait Posture 34 (3):347–351 Mittlmeier T, Weiler A, Söhn T, Kleinhans L, Mollbach S, Duda G, Südkamp NP (1999) Functional monitoring during rehabilitation following anterior cruciate ligament reconstruction. Clin Biomech (Bristol, Avon) 14 (8):576–584 Molund M, Husebye EE, Hellesnes J, Nilsen F, Hvaal K (2018) Proximal medial gastrocnemius recession and

22 stretching versus stretching as treatment of chronic plantar heel pain. Foot Ankle Int 39(12):1423–1431 Nam SH, Son SM, Kim K (2017) Changes of gait parameters following constrained-weight shift training in patients with stroke. J Phys Ther Sci 29(4):673–676 Notarnicola A, Maccagnano G, Fiore A, Spinarelli A, Montenegro L, Paoloni M, Pastore F, Tafuri S, Moretti B (2018) Baropodometry on patients after total knee arthroplasty. Musculoskelet Surg 102(2):129–137 Oliveira AKA, Borges DT, Lins CAA, Cavalcanti RL, Macedo LB, Brasileiro JS (2016) Immediate effects of Kinesio Taping(®) on neuromuscular performance of quadriceps and balance in individuals submitted to anterior cruciate ligament reconstruction: a randomized clinical trial. J Sci Med Sport 19(1):2–6 Ozkan H, Ege T, Koca K, Can N, Yurttas Y, Tunay S (2016) Pedobarographic measurements after repair of Achilles tendon by minimal invasive surgery. Acta Orthop Belg 82(2):271–274 Paolucci T, Pezzi L, Mannocci A, La Torre G, Bellomo RG, Saggini R (2020) Flat foot and postural harmony in 6-year-old Caucasians: what is their relationship? Ann Rehabil Med 44(4):320–326 Pauk J, Ihnatouski M, Daunoraviciene K, Laskhousky U, Griskevicius J (2016) Research of the spatial-temporal gait parameters and pressure characteristic in spastic diplegia children. Acta Bioeng Biomech 18 (2):121–129 Rogers A, Morrison SC, Gorst T, Paton J, Freeman J, Marsden J, Cramp MC (2020) Repeatability of plantar pressure assessment during barefoot walking in people with stroke. J Foot Ankle Res 13(1):39 Rongies W, Bak A, Lazar A, Dolecki W, KolanowskaKenczew T, Sierdziński J, Spychała A, Krakowiecki A (2009) A trial of the use of pedobarography in the assessment of the effectiveness of rehabilitation in patients with coxarthrosis. Ortop Traumatol Rehabil 11(3):242–252

J. Lorkowski and K. Gawronska Schuh R, Hofstaetter SG, Adams SB Jr, Pichler F, Kristen KH, Trnka HJ (2009) Rehabilitation after hallux valgus surgery: importance of physical therapy to restore weight bearing of the first ray during the stance phase. Phys Ther 89(9):934–945 Schuh R, Adams S, Hofstaetter SG, Krismer M, Trnka HJ (2010) Plantar loading after chevron osteotomy combined with postoperative physical therapy. Foot Ankle Int 31(11):980–986 Șerban O, Papp I, Bocșa CD, Micu MC, Bădărînză M, Albu A, Fodor D (2020) Do ankle, hindfoot, and heel ultrasound findings predict the symptomatology and quality of life in rheumatoid arthritis patients? J Ultrason 20(81):e70–e82 Skopljak A, Muftic M, Sukalo A, Masic I, Zunic L (2014) Pedobarography in diagnosis and clinical application. Acta Inform Med 22(6):374–378 Telfer S, Bigham JJ (2019) The influence of population characteristics and measurement system on barefoot plantar pressures: a systematic review and metaregression analysis. Gait Posture 67:269–276 Vette AH, Funabashi M, Lewicke J, Watkins B, Prowse M, Harding G, Silveira A, Saraswat M, Dulai S (2019) Functional, impulse-based quantification of plantar pressure patterns in typical adult gait. Gait Posture 67:122–127 Xiang L, Mei Q, Fernandez J, Gu Y (2020) A biomechanical assessment of the acute hallux abduction manipulation intervention. Gait Posture 76:210–217 Yoon SW (2015) Effect of the application of a metatarsal bar on pressure in the metatarsal bones of the foot. J Phys Ther Sci 27(7):2143–2146 Yuan XN, Liang WD, Zhou FH, Li HT, Zhang LX, Zhang ZQ, Li JJ (2019) Comparison of walking quality variables between incomplete spinal cord injury patients and healthy subjects by using a foot scan plantar pressure system. Neural Regen Res 14 (2):354–360

Adv Exp Med Biol - Clinical and Experimental Biomedicine (2022) 15: 23–28 https://doi.org/10.1007/5584_2021_697 # The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 Published online: 18 January 2022

Quantum Medicine: A Role of Extremely Low-Frequency Magnetic Fields in the Management of Chronic Pain Giovanni Barassi , Mieczyslaw Pokorski , Raffaello Pellegrino , Marco Supplizi, Loris Prosperi , Celeste Marinucci , Edoardo Di Simone , Chiara Mariani, Alì Younes, and Angelo Di Iorio Abstract

Extremely low-frequency electromagnetic field (ELF-MF) therapy is a promising treatment for chronic pain, given its ability to interact with body homeostasis using watermediated transmission mechanisms typical of quantum medicine. The present study aims to assess the effects of ELF-MF therapy on

G. Barassi (*), M. Supplizi, L. Prosperi, C. Marinucci, E. Di Simone, C. Mariani, and A. Younes Center for Physiotherapy, Rehabilitation, and Reeducation (Ce.Fi.R.R.) of the Center of Sports Medicine, “G. d’Annnunzio” University, Chieti, Italy e-mail: [email protected]; [email protected] M. Pokorski Institute of Health Sciences, Opole University, Opole, Poland e-mail: [email protected] R. Pellegrino Antalgic Mini-Invasive and Rehab–Outpatients Unit, Department of Medicine and Science of Aging, “G. d’Annunzio” University, Chieti, Italy A. Di Iorio Department of Medicine and Science of Aging, Center of Sports Medicine, “G. d’Annunzio” University, Chieti, Italy e-mail: [email protected]

chronic pain in 49 patients suffering from various musculoskeletal disorders. The therapy was applied through a Quec Phisis setup generating the electromagnetic field as the ion cyclotronic resonance. Patients underwent eight therapy sessions of 45 min each performed every other day. The bioimpedance assessment was based on the comparison of the height-adjusted body resistance (R/h) and reactance (Xc/h) measured during the first and last sessions of eight-session treatment. Pain perception was quantified using the standard visual-analog scale. We found significant increases in both R/h and Xc/h parameters of body bioimpedance after electromagnetic therapy corresponding with reductions in pain perception. We conclude that the ELF-MF therapy can restore the body’s state of health and thus seems a valid therapeutic approach for the treatment of musculoskeletalderived pain. Keywords

Chronic pain · Electromagnetic stimulation · Magnetotherapy · Pain management · Rehabilitation

23

24

1

G. Barassi et al.

Introduction

Electromagnetic field (EMF) therapy is a noninvasive and safe method widely used to relieve pain in musculoskeletal diseases. EMF therapy increases the content of brain beta-endorphins that elevate the pain threshold in humans. The reduced pain sensation may last for up to 4 weeks after therapy (Yamaguchi-Sekino et al. 2011). The use of magnetotherapy has recently increased, particularly in the treatment of chronic low-back pain, fibromyalgia, polymyoarthralgia, arthrosis, ossification disturbances, osteoporosis, peripheral joints degeneration, rheumatoid arthritis, or ankylosing spondylitis (Shupak et al. 2006; Fujita et al. 2001a). The efficacy of EMF therapy depends on the type of electromagnetic fields used. Notably, the extremely low-frequency magnetic fields (ELF-MF) with a frequency lower than 100 Hz and a low amplitude can improve neurotransmission, local immunity, mutual antigen-antibody relationships, and cell membrane permeability, which all act to balance the ion trafficking across cellular membranes (Barassi et al. 2020; Corbellini et al. 2014; Bistolfi 2007). The natural activity of the body per se generates ELF that may have soothing and balancing homeostatic effects. Therefore, the therapeutic effect of ELF lies in the synchronization between the externally applied electromagnetic field and that generated by the biological system receiving the application, the latter usually altered by a pathology. The ELF-MF therapy, which fits into quantum medicine, can remove the body magnetic disturbance that underlies changes in molecular informational trafficking. The interaction between ELF electromagnetic fields and living organisms has been called the cyclotronic resonance by Liboff (2019). This interaction influences the properties of cell membranes, electrolyte systems, sensitivity threshold for free nerve endings, and cells capable of contracting (Adey 2004; Polk et al. 1996). Additionally, cyclotronic resonance can influence cellular inflammatory and neurological processes (Del Seppia et al. 2007).

Quantum medicine encompasses the concept of coherence domain, i.e., the aggregation and cooperation of specific molecules for a biological purpose. This concept has to do with the ability of body water molecules to coherently organize themselves in response to the influence of electromagnetic fields using the “resonance between fields” mechanism. An aqueous system such as the human body could play a role in modulating biological functions by providing the basis for the processing, storage, and retrieval of information mediated by electromagnetic signals that mimic the effect of drugs or guide endogenous functions. Water molecules store information by aggregating in the form of clusters, having their electromagnetic fields, which release or exchange molecules with other liquid entities coming into the physical contact. The interface for the exchange is the surrounding electromagnetic fields. The Zhadin effect shows that the application of specific combinations of electromagnetic fields with different frequencies can modify the ionic flow (Zhadin et al. 1998, Zhadin and Fesenko 1990; Gerardi et al. 2008). Such reactions are created through cyclotron resonance or ion resonance. The relationship between pain and skin impedance is widely discussed in the literature (Fujita et al. 2001b; Abu Khaled et al. 1988). The bioimpedance is classically used to identify body composition. Studies have shown a reduction in skin impedance in painful conditions (Böhm and Heitmann 2013; Hampf 1990) due likely to autonomic changes triggered by pain such as sebaceous and sweat secretions (Yamamoto et al. 2006). Low values of resistance and reactance may indicate an inflammatory state (Baumgartner et al. 1988), oxidative stress (Chuang et al. 2006; Crouser 2004), mitochondrial dysfunction, and apoptosis (Garrabou et al. 2012; Galley 2011). Conversely, high values of these parameters indicate a good health status. Given the known therapeutic role of ion cyclotronic resonance therapy, the present study aims to assess how it influences body impedance represented by resistance (R/h) and reactance

Quantum Medicine: A Role of Extremely Low-Frequency Magnetic Fields in the. . .

(Xc/h) in patients suffering from chronic musculoskeletal pain.

2

Methods

2.1

Patients and Study Design

The is a single-center, retrospective observational study conducted at the Center for Physiotherapy, Rehabilitation, and Reeducation (Ce.Fi.R.R.), part of the Center of Sports Medicine of the “Gabriele d’Annunzio” University in Chieti, Italy. We included 49 outpatients (M/F, 9/40; mean age, 56.3
18.1 years) suffering from musculoskeletal chronic pain due to the low-back pain syndrome, fibromyalgia, degeneration of peripheral joints, arthrosis, and polymyoarthralgia which lasted for more than 3 months. Exclusion criteria were the following: the use of drugs and the presence of immune system-related diseases, bladder and bowel dysfunctions, pacemakers, pregnancy, cancer disease, vascular pathologies, hematomas, peripheral and central nervous system pathologies, systemic lymphatic pathologies, infections, epilepsy, or history of mental disorders, all of which could limit the adherence to the trial procedures. All patients underwent eight ion cyclotronic resonance therapy sessions of 45 min each performed every other day for 15 days (eight sessions). The treatment was performed using a Quec Phisis-1 bioelectric impedance device (Prometeo S.r.l., Padua, Italy). The patient lay in the supine position on a bed surrounded by four equidistant electromagnetic coils (97 cm in diameter) placed from the head to feet. The device emits the ELF electromagnetic field between 0.01 and 10 times that of the Earth’s electromagnetic field of 40–50 mT, with a frequency range between 0.3 and 80 Hz, and tracks R/h and Xc/h in real time following the metabolic effects due to the stimulus-induced activation of body ions. To ensure the highest accuracy, the device automatically performs an environmental geomagnetic reading to realign the measurement to the local electromagnetic field and sets the ELF electromagnetic field to an individual body requirement.

25

To the latter end, the patient answers a questionnaire about the health status before the first session, and the answers were integrated into the device’s software. The same device assesses bioimpedance in real time. In this study, body bioimpedance was expressed by the heightadjusted R/h and Xc/h and was measured during the first (baseline) and last (follow-up) sessions of an eight-session therapy. The measurement was performed with four electrodes, two each attached to the right hand and foot. Patients were fasting for at least 8 h before all trials. The pain was assessed and quantified using the standard visual-analog scale (VAS) with a “0” score denoting no pain and “10” denoting the maximum pain perceived. In this study, neither randomization nor blinding was performed since all sessions had the same nature and settings, and both patients and therapists were informed and have known the procedures. Additionally, data collection was performed according to the standard operative routine of the ion cyclotronic resonance device. Data were expressed as means
SD of R/h and Xc/h in Ω/m. Statistical elaboration consisted of linear mixed models for repeated measures. All tests were two-sided. The mean difference in the pain score was compared using a paired t-test. A p-value