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

Table of contents :
Contents
Artificial Intelligence and Precision Medicine: A Perspective
1 Introduction
2 Methods
3 Genetics, Omics, and Precision Medicine
4 Oncology
5 Radiology and Radiogenomics
6 Artificial Intelligence, Precision Medicine, and Recent COVID-19 Pandemic
7 Sensitivity of Genomic Data Concerning Public Health
8 Summary and Conclusions
References
Pedobarography in Physiotherapy: A Narrative Review on Current Knowledge
1 Background
2 Database Search
3 Search Results
4 Physiotherapy in Orthopedic Diseases
5 Physiotherapy in Neurologic Diseases
6 Physiotherapy in Diabetes Mellitus
7 Pedobarography in Rheumatology
8 New Technologies in Rehabilitation
9 Conclusions
References
Quantum Medicine: A Role of Extremely Low-Frequency Magnetic Fields in the Management of Chronic Pain
1 Introduction
2 Methods
2.1 Patients and Study Design
3 Results
4 Discussion
References
Manual Therapy Approach to the Extraocular Muscles in Migraine Treatment: A Preliminary Study
1 Introduction
2 Methods
2.1 Patients and Study Design
2.2 Therapeutic Interventions
2.3 Statistical Analysis
3 Results
4 Discussion
References
Capacitive and Resistive Electric Transfer Therapy: A Comparison of Operating Methods in Non-specific Chronic Low Back Pain
1 Introduction
2 Methods
2.1 Patients and Study Design
2.2 Treatment Interventions
2.2.1 Thermal Imaging
2.2.2 Digital Pressure Algometry
2.3 Statistical Analysis
3 Results
4 Discussion
References
Serum Impedance in Children with Recurrent Respiratory Infections
1 Introduction
2 Methods
3 Results
4 Discussion
References
Olfactory Response to Altitude Hypoxia: A Pilot Study During a Himalayan Trek
1 Introduction
2 Methods
2.1 Statistical Analysis
3 Results
4 Discussion
References
The Impact of Change in Hospital Admissions When Primary Care Is Provided by a Single Primary Care Physician: A Cohort Study A...
1 Introduction
1.1 Intervention
1.2 Outcome Measures
1.3 Sensitivity Analysis
2 Results
3 Comments
4 Study Limitations
References
Risk of Falls in Patients Aged Over 65 in the Context of the Treatment Facility
1 Introduction
2 Methods
2.1 Study Design and Participants
2.2 Data Analysis
3 Results
4 Discussion
References
Anthropometric Indices as Long-Term Predictors of Diabetes in Impaired Fasting Glucose Metabolism: Findings in the PURE Study ...
1 Introduction
2 Methods
2.1 Study Design
2.2 Participants and Measurements
2.3 Statistical Analysis
3 Results
4 Discussion
References
Cardiovascular Risk Factors Drive Impaired Fasting Glucose to Type 2 Diabetes: Findings After a 9-Year Follow-Up in the PURE S...
1 Introduction
2 Methods
2.1 Study Design and Participants
2.2 Measurements
2.3 Statistical Analysis
3 Results
4 Discussion
References
Sleep-Disordered Breathing in Pregnancy
1 Introduction
2 Methods
3 Results
4 Discussion
References

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

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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

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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

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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

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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

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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

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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.

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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.

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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

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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

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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.

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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).

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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).

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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

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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

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

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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.

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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

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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.

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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

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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

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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

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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

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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).

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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).

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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).

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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

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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.

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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

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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.

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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.

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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

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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.

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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.

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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