Internet of Medicine for Smart Healthcare E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 890

Veröffentlichungsjahr: 2024

Beschreibung

This book provides in-depth explanations and discussions of the latest applications of Artificial Intelligence (AI), machine learning, and the Internet of Medicine, offering readers the cutting edge on this rapidly growing technology that has the potential to transform healthcare and improve patient outcomes.

Over the past five years, there have been significant advances in healthcare through the use of artificial intelligence (AI) and machine learning (ML) technologies. AI and machine learning in medical imaging has significantly improved the accuracy and speed of medical imaging analysis, accelerated the drug discovery process by identifying potential drug targets and predicting the efficacy and safety of new drugs, and enabled personalized medicine by analyzing large amounts of patient data to identify individualized treatment plans based on a patient’s genetic makeup and medical history. Internet of Medicine (IoM) refers to the integration of the Internet of Things (IoT) and connected medical devices with healthcare systems and processes to enable remote monitoring, diagnosis, and treatment of patients. IoM is a subset of the larger Internet of Things concept, which involves the connection of everyday devices and appliances to the internet for various purposes. IoM has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing efficiency. Some of the specific applications of IoM include remote patient monitoring, real-time data analysis, predictive analytics, smart hospitals, and personalized medicine.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Omics Data Integration in AI System for Immediate and Carryover Effects of Neurodynamic Exercises on SLR Ranges Among Acute PIVD Patients

1.1 Introduction

1.2 Literature Review

1.3 Methodology

1.4 Hypothesis

1.5 Procedure

1.6 Intervention

1.7 Data Analysis

1.8 Result

1.9 Discussion

1.10 Conclusion

References

2 Effectiveness of Graph-Based Methods for Biological Networks for Primal Reflex Release Techniques on Pain and Disability in Cervicogenic Headache Patient

2.1 Introduction

2.2 Literature Review

2.3 Methodology

2.4 Procedure

2.5 Conclusion

References

3 Application of AI in Determining Immediate and Carryover Effects of Primal Reflex Release Technique Neural Reboot on SI Joint Mobility

3.1 Introduction

3.2 Literature Review

3.3 Methodology

3.4 Results

3.5 Conclusion

References

4 Future Trends in Bioinformatics AI Integration for Analyzing Immediate Effect of Primal Reflex Release Technique in Neck Pain and Stiffness Patients

4.1 Introduction

4.2 Literature Review

4.3 Methodology

4.4 Conclusion

References

5 Evolutionary Computation in Bioinformatics Analyzing the Effects of Neurodynamic Exercises on Pain and Disability in Carpal Tunnel Syndrome Patients

5.1 Introduction

5.2 Literature Review

5.3 Methodology

5.4 Result and Discussion

5.5 Conclusion

References

6 Imperative Role of Artificial Intelligence and Nanotechnology in Healthcare Sector for Sustainable Development

6.1 Introduction

6.2 Literature Review

6.3 AI Applications in Healthcare Industries

6.4 Application of AI and Nanotechnology in Medicine

6.5 AI and Nanotechnology in Anti-Aging Medicines

6.6 Result

6.7 Discussion

6.8 Conclusion

References

7 Holistic Approaches for Sleep Pattern Enhancement Using AI and Yoga Therapy: A Comprehensive Scientific Approach

7.1 Introduction

7.2 Understanding Sleep Patterns

7.3 The Importance of Quality Sleep

7.4 The Role of AI in Sleep Pattern Enhancement

7.5 How AI Works to Enhance Sleep

7.6 How Yoga Influences Sleep

7.7 Combining AI and Yoga for Better Sleep

7.8 The Synergy of AI and Yoga

7.9 Scientific Research on Sleep Improvement

7.10 The Mind–Body Connection

7.11 A Holistic Approach to Sleep

7.12 Practicing Yoga for Better Sleep

7.13 AI-Enabled Sleep Tracking Devices

7.14 Personalized Sleep Plans

7.15 Lifestyle Factors and Sleep

7.16 Conclusion

References

8 Ethical Consideration in Bioinformatics in AI for Analyzing the Effects of Using SHAT Device on Upper Extremity Functions in Stroke Patients

8.1 Introduction

8.2 SHAT Device (Synchronized Hand Arm Training Device)

8.3 How to Perform SHAT Exercises

8.4 Literature Review

8.5 Methodology

8.6 Protocol

8.7 Procedure

8.8 Results

8.9 Discussion

8.10 Conclusion

References

9 AI-Driven Drug Discovery and Repurposing for Analyzing Long-Term Effects of Nerve Sliders and Tensioners on Quality of Life in Cervicogenic Headache Patients

9.1 Introduction

9.2 Literature Review

9.3 Methodology

9.4 Protocol

9.5 Procedure

9.6 Discussion

9.7 Conclusion

References

10 Using Artificial Intelligence (AI) Analyzing Recent Advancements in the Anti-Cancerous Properties of Edible Mushrooms and Their Association with the Mode of Action of Polysaccharides

10.1 Introduction

10.2 Polysaccharide Metabolism and Bioavailability

10.3 Conclusion

References

11 Impact of Artificial Intelligence (AI) in Bioremediation of Dairy Effluent by Microalgae and the Potential Application of the Produced Lipid Byproducts

11.1 Introduction

11.2 Microalgae

11.3 Lipid-Producing Microalgal Strains

11.4 Biosynthesis of Lipid in Microalgae

11.5 Applications of Microalgal Lipids

11.6 Challenges in the Field of Microalgal Biomass Productivity

11.7 Conclusion

References

12 Smart Collision Recognition and Reporting System with GPS and GSM Integration

12.1 Introduction

12.2 Literature Review

12.3 Block Diagram

12.4 Methodology

12.5 Conclusion

References

13 Evolution and Impact of Wearable Devices in Healthcare: Anatomy of Wearable Technology and its Influence on Medical Sciences

13.1 Introduction

13.2 Historical Development of Wearable Technology

13.3 Anatomy of Wearable Technology

13.4 Types of Wearable Devices in Healthcare

13.5 Applications of Wearable Technology in Medical Sciences

13.6 Impact of Wearable Technology on Healthcare

13.7 Future Trends and Innovations in Wearable Technology

13.8 Conclusion

References

14 Current State of Wearable Healthcare Technology: Physiology and Biochemistry of Wearable Sensors and Devices

14.1 Introduction

14.2 Physiological Monitoring

14.3 Biochemical Monitoring

14.4 Integration of Sensors and Devices

14.5 Applications in Healthcare

14.6 Advances in Data Analytics and Artificial Intelligence

14.7 Challenges and Future Directions

14.8 Conclusion

References

15 Real-Time Data Acquisition and Analysis Techniques: Microbiological and Immunological Aspects of Real-Time Health Data Collection

15.1 Introduction

15.2 Real-Time Microbiological Data Collection

15.3 Real-Time Immunological Data Collection

15.4 Technology and Tools for Real-Time Data Collection

15.5 Data Analysis Techniques

15.6 Case Studies and Applications

15.7 Future Directions and Challenges

15.8 Conclusion

References

16 Applications of Real-Time Data in Healthcare Interventions: Pharmacological Interventions Based on Real-Time Data Analytics

16.1 Introduction

16.2 Personalized Medicine

16.3 Treatment Efficacy

16.4 Proactive Interventions

16.5 Clinical Trials

16.6 Challenges and Future Directions

16.7 Conclusion

References

17 Strategies for Integrating Wearable Technology with Healthcare Systems: Pathological Considerations in Wearable Device Integration

17.1 Introduction

17.2 Pathological Considerations in Wearable Device Integration

17.3 Strategies for Effective Integration

17.4 Case Studies and Examples

17.5 Future Directions and Emerging Trends

17.6 Conclusion

References

18 Challenges and Solutions in Healthcare System Integration: Histological Perspectives on Wearable Device Integration Challenges

18.1 Introduction

18.2 Challenges in Integrating Wearable Devices

18.3 Interoperability Challenges

18.4 Security and Privacy Concerns

18.5 Accuracy and Reliability of Data

18.6 Scalability and Sustainability

18.7 Solutions to Integration Challenges

18.8 Case Studies and Best Practices

18.9 Future Trends and Outlook

18.10 Conclusion

References

19 Role of Wearable Technology in Mental Health Monitoring and Management: Psychiatric Insights into Wearable Technology Adoption

19.1 Introduction

19.2 Adoption of Wearable Technology in Mental Healthcare

19.3 Data Privacy and Security in Mental Healthcare

19.4 Potential Impact of Wearable Technology in Mental Healthcare

19.5 Case Studies and Examples

19.6 Future Directions and Challenges

19.7 Conclusion

References

20 Ethical Considerations in Mental Health Data Collection and Analysis: Ethical and Legal Aspects of Mental Health Data in Wearable Technology

20.1 Introduction

20.2 Ethical Principles in Mental Health Data Collection and Analysis

20.3 Legal Frameworks for Mental Health Data in Wearable Technology

20.4 Informed Consent in Wearable Technology

20.5 Data Security and Privacy in Mental Health Wearables

20.6 Potential Misuse of Mental Health Data

20.7 Ensuring Equity in Access to Mental Health Wearables

20.8 Ethical Guidelines for Researchers and Practitioners

20.9 Conclusion

References

21 Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment

21.1 Introduction

21.2 Ethical Principles in AI Deployment

21.3 Challenges in AI Deployment in Urology and Gastroenterology

21.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology

21.5 Case Studies

21.6 Future Directions and Recommendations

21.7 Conclusion

References

22 Future Directions and Opportunities in AI-Driven Healthcare: Family Medicine and Anesthesiological Future Directions in AI-Driven Healthcare

22.1 Introduction

22.2 AI Applications in Family Medicine

22.3 AI Applications in Anesthesiology

22.4 Current Challenges and Limitations

22.5 Future Directions and Opportunities

22.6 Case Studies and Examples

22.7 Conclusion

References

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Mean age, sex, and side.

Table 1.2 Mean and standard deviation of age in both groups.

Table 1.3 Pre-post data within the experimental group.

Table 1.4 Post and carryover data within the experimental group.

Table 1.5 Pre-post data within the control group.

Chapter 2

Table 2.1 Within-group comparison of outcome measure in group A (mean ± SD), P...

Table 2.2 Within-group comparison of outcome measure in group B (mean ± SD), P...

Table 2.3 Within-group comparison of outcome measure pre-post in group A (mean...

Table 2.4 Within-group comparison of outcome measure pre-post in group A (mean...

Table 2.5 Between group comparison outcome measure (VAS and NDI) in groups A a...

Table 2.6 Cochran’s Q-test for OMNE.

Chapter 3

Table 3.1 Gender distribution in groups A and B.

Table 3.2 Mean age distribution in both groups A and B.

Table 3.3 Mean comparison of VAS pre, post, and carryover scores for groups A ...

Table 3.4 Within-group comparison of outcome measure from pre to post in group...

Table 3.5 Within-group comparisons of outcome measured from post to carryover ...

Table 3.6 Within-group comparison of outcome measure from pre to post in group...

Table 3.7 Within-group comparison of outcomes measured from post to carryover ...

Table 3.8 P-value of between-group comparison of post data of outcome measures...

Table 3.9 P-value of between-group comparison of carryover data of outcome mea...

Chapter 4

Table 4.1 Age mean and standard deviation.

Table 4.2 Comparison of NPRS pre and post reading for the experimental and con...

Table 4.3 Post data of NPRS for control and experimental groups.

Table 4.4 Comparison of cervical extension range of motion for the experimenta...

Table 4.5 Post data of ROM extension.

Table 4.6 Comparison of cervical flexion range motion between the experimental...

Table 4.7 Post data of ROM flexion.

Chapter 5

Table 5.1 Mean and standard deviation of VAS score between both groups.

Table 5.2 Mean and standard deviation of NDI score between both groups.

Table 5.3 Mean and standard deviation of CV angle range between both groups.

Table 5.4 Mean and standard deviation of ipsilateral cervical lateral rotation...

Table 5.5 Mean and standard deviation of cervical extension range between both...

Table 5.6 Mean and standard deviation of ipsilateral lateral flexion range bet...

Chapter 6

Table 6.1 Intervention of artificial intelligence in medical practices.

Table 6.2 Analysis of previous studies.

Chapter 8

Table 8.1 Gender and age frequency.

Table 8.2 Mean and SD of control group pre and post.

Table 8.3 Comparison of the experimental group pre and post.

Table 8.4 Mean and SD of post scores of the experimental group.

Chapter 9

Table 9.1 Gender and age frequency.

Table 9.2 Within-group comparison of outcome measure in group A.

Table 9.3 Within-group comparison of outcome measure in group B.

Table 9.4 Difference between the groups (post-data).

Table 9.5 Within-group comparison of outcome measure in group A (mean ± SD).

Table 9.6 Within-group comparison of outcome measure in group B (mean ± SD).

Chapter 10

Table 10.1 The role of ganoderma polysaccharides in biological processes and t...

Chapter 11

Table 11.1 Classification of microalgae based on their pigment and reserve foo...

Table 11.2 Some lipid-containing microalgal species [7–18].

Chapter 12

Table 12.1 Survey of some systematic study design vehicle safety [11–21].

Chapter 13

Table 13.1 Summary of different wearable device.

Chapter 15

Table 15.1 Summary of real-time data acquisition and analysis.

Chapter 17

Table 17.1 Statistical data showing for integration of wearable device in heal...

Chapter 18

Table 18.1 Summary of different security and privacy concerns.

Chapter 20

Table 20.1 Summary of privacy and safety of personal health information.

List of Illustrations

Chapter 1

Figure 1.1 Universal goniometer.

Figure 1.2 Stopwatch.

Figure 1.3 Testing of SLR.

Figure 1.4 Measuring of SLR range.

Figure 1.5 Mean age, sex, and side in both groups (baseline data for demograph...

Figure 1.6 Pre-post data within the experimental group.

Figure 1.7 Post-carryover data within the experimental group.

Chapter 2

Figure 2.1 Janda’s upper crossed syndrome

Figure 2.2 Comparison of NDI-pre and NDI-post treatments, group A (mean and SD...

Figure 2.3 Comparison of VAS-pre and VAS-post treatments, group A (mean and SD...

Figure 2.4 Comparison of VAS-pre and VAS-post treatments, group B (mean and SD...

Figure 2.5 Comparison of NDI-pre and NDI-post treatments, group B (mean and SD...

Figure 2.6 Pre and post data for group A OMNE.

Figure 2.7 Pre and post data for group B OMNE.

Chapter 3

Figure 3.1 Gender distributions.

Figure 3.2 Graphical representation of mean age distribution.

Figure 3.3 Graphical representation of VAS.

Chapter 4

Figure 4.1 Illustrations of workouts using guidance from the AI-assisted healt...

Figure 4.2 NPRS pre and post reading for the experimental group.

Figure 4.3 Pre and post data of NPRS.

Figure 4.4 Pre and post mean and SD of the control group.

Figure 4.5 Post data of the experimental and control groups.

Figure 4.6 Post data of the control and experimental groups’ ROM extension.

Figure 4.7 Pre and post data of ROM flexion.

Figure 4.8 Post data of control and experimental groups.

Figure 4.9 Pre and post data of ROM flexion control group.

Figure 4.10 Pre and post data of the experimental group.

Figure 4.11 Pre and post data of the control group.

Chapter 5

Figure 5.1 Distribution of the median nerve in the palmar aspect of the right ...

Figure 5.2 Pre and post mean and SD graph of VAS of both groups.

Figure 5.3 Pre and post mean and SD graph of NDI score of both groups.

Figure 5.4 Pre and post mean and SD graph of CV angle of both groups.

Figure 5.5 Pre and post mean and SD graph of ipsilateral cervical lateral rota...

Figure 5.6 Pre and post mean and SD graph of cervical extension of both groups...

Figure 5.7 Pre and post mean and SD graph of ipsilateral cervical lateral flex...

Chapter 6

Figure 6.1 The role of AI in providing efficient healthcare services to the pa...

Figure 6.2 Review of database from depository.

Figure 6.3 The achievement of SDG goals with integration of AI and nanotechnol...

Figure 6.4 Application of AI in healthcare industries.

Figure 6.5 Integration of AI and nanotechnology for development of medicine.

Figure 6.6 Utilizing AI and nanotechnology to improve clinical trials.

Chapter 7

Figure 7.1 Holistic approach to sleep.

Figure 7.2 Sleep tracking device simulation.

Chapter 8

Figure 8.1 Important actions for an ED-based stroke machine learning decision ...

Figure 8.2 Mean age in both groups.

Figure 8.3 Comparison of control group pre and post scores.

Figure 8.4 Comparison of experimental group pre and post scores.

Figure 8.5 Comparison of post scores of the experimental groups.

Figure 8.6 Comparison of post scores between the experimental and control grou...

Chapter 9

Figure 9.1 Displays the human and preclinical pain biomarkers [10].

Figure 9.2 Gender distribution within groups.

Figure 9.3 Gender distribution within groups.

Figure 9.4 Within-group comparison of outcome measure in group A.

Figure 9.5 Within-group comparison of outcome measure in group B.

Figure 9.6 Quality-of-life improvement in data.

Figure 9.7 Quality-of-life improvement within the group.

Chapter 11

Figure 11.1 Biosynthesis of lipids in microalgae [20].

Figure 11.2 Production and applications of microalgal lipids [7].

Figure 11.3 Structures of polyunsaturated fatty acid (omega 3 and 6 essential ...

Figure 11.4 Structure of commonly occurring carotenoids in microalgae.

Figure 11.5 Structure of commonly occurring oxylipins (oxidized PUFA) in micro...

Chapter 12

Figure 12.1 Schematic of vehicle collision recognition and alert system.

Chapter 13

Figure 13.1 Overview of anatomy of wearable technology and its influence on me...

Figure 13.2 Wearable device technology in the future.

Figure 13.3 Different type of wearable device in healthcare.

Figure 13.4 Illustration of wearable drug delivery systems.

Chapter 14

Figure 14.1 Overview of different parts of wearable healthcare technology.

Figure 14.2 Illustration of the process of a heart rate monitoring system.

Figure 14.3 Overview of different applications in healthcare.

Figure 14.4 Illustration of health monitoring system.

Chapter 15

Figure 15.1 Overview of structure for real-time data acquisition and analysis ...

Figure 15.2 Illustration of the process of monitoring microbial populations in...

Figure 15.3 Illustration of the structure of a system for real-time immunologi...

Figure 15.4 Representation of modern personalised medicine.

Chapter 16

Figure 16.1 Overview of real-time data in healthcare interventions.

Figure 16.2 Illustration of the process steps involved in using real-time data...

Figure 16.3 Illustration of the system architecture for continuous monitoring ...

Figure 16.4 Illustration of the architecture of predictive analytics for early...

Chapter 17

Figure 17.1 Overview of wearable devices with healthcare systems.

Figure 17.2 Illustration of earlier system in healthcare.

Figure 17.3 Illustration of the integration process with electronic health rec...

Figure 17.4 Detection of user data and health monitoring.

Figure 17.5 Distribution of diseases in patient groups.

Chapter 18

Figure 18.1 Illustration of wearable devices in healthcare and their applicati...

Figure 18.2 Security and privacy concern in healthcare systems.

Figure 18.3 Representation of EHR in healthcare system.

Figure 18.4 Overview of cloud-based solution for healthcare systems.

Figure 18.5 Overview of sensor technology improvement in healthcare.

Chapter 19

Figure 19.1 Overview of mental healthcare system.

Figure 19.2 Adoption of wearable healthcare technology by consumers.

Figure 19.3 Overview of psychiatric insights into secure data management syste...

Figure 19.4 Illustration of process flow for wearable technology in mental hea...

Chapter 20

Figure 20.1 Overview of ethical decision-making system.

Figure 20.2 Architecture of wearable technology in mental health.

Chapter 21

Figure 21.1 Overview of ethical challenges and guidelines for AI deployment in...

Figure 21.2 Representation of urological and gastroenterological perspectives ...

Figure 21.3 Overview of ethical AI deployment in urology and gastroenterology.

Chapter 22

Figure 22.1 Overview of family medicine and anesthesiological in AI-driven hea...

Figure 22.2 AI-driven healthcare system.

Figure 22.3 Overview of anesthesia drug dosing and management.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

About the Editors

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Internet of Medicine for Smart Healthcare

Edited by

Abhishek Kumar

Narayan Vyas

Pramod Singh Rathore

Abhineet Anand

and

Pooja Dixit

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-27223-5

Front cover images supplied by Pixabay.comCover design by Russell Richardson

Preface

This book is organized into 22 chapters. In Internet of Medicine (IOM) for Smart Healthcare, we usher in a dialogue that melds the computational prowess of artificial intelligence (AI) with the nuanced realm of healthcare. Within 22 chapters, each is a beacon of innovation, exploring how AI is reshaping patient care, from diagnostics to treatment and beyond. As the editors, we have witnessed a tapestry of research that not only pushes the boundaries of technology but also tethers it closely to the human condition. From the micro-scale of genetic data to the macro-impact of wearable technology, the contributions herein address both the potential and the challenges of these converging worlds, underlining the importance of ethical considerations as we tread forward. This book stands as an invitation to embrace the future—a future where technology and healthcare converge to better human lives.

Delving into the intersection of omics data and AI, Chapter 1 illustrates how integrating vast biological datasets can refine neurodynamic exercises for PIVD patients, offering insights into personalized care strategies that yield both immediate and lasting therapeutic effects. Chapter 2, based on the exploration of graph-based methods in biological networks, reveals how AI can enhance the treatment of cervicogenic headache patients, optimizing reflex release techniques to reduce pain and disability more effectively than ever before. Focusing on the potent synergy of AI with primal reflex release techniques, Chapter 3 discusses the role of AI in quantifying the effects of such interventions on SI joint mobility, providing data-driven insights for clinicians. Next, Chapter 4 probes into the future of AI in bioinformatics, projecting its transformative impact on understanding and addressing neck pain and stiffness through innovative reflex release techniques. In Chapter 5, evolutionary computation underscores the value of bioinformatics in dissecting the effects of neurodynamic exercises, offering new avenues for treating pain and disability in carpal tunnel syndrome patients. In Chapter 6, AI and nanotechnology are presented as pivotal drivers for sustainable development in healthcare, with discussions centered on their imperative roles in ushering a new era of medical innovation. Chapter 7 basically synthesizes AI and yoga therapy as holistic approaches to sleep pattern enhancement, presenting a scientific approach that could redefine therapeutic strategies for better health. Next, Chapter 8 focused on ethical considerations in AI, and bioinformatics are dissected in the context of analyzing the rehabilitation effects of the SHAT device on stroke patients, underscoring the importance of ethical data handling. In Chapter 9, the AI’s transformative impact on drug discovery and repurposing is discussed, with a focus on its potential to improve the long-term quality of life for patients with cervicogenic headaches. In Chapter 10, the antineoplastic potential of edible mushrooms is examined through the lens of AI, showcasing how advancements in AI can elucidate the polysaccharides’ modes of action and their health benefits. Chapter 11 evaluates the role of AI in enhancing the bioremediation processes of dairy effluent using microalgae, highlighting the innovative use of resulting lipid byproducts. Chapter 12 focused on the smart collision recognition, and reporting system employing GPS and GSM technology is introduced, signifying how AI can leverage automation to enhance safety measures. In Chapter 13, the evolution and consequential impact of wearable devices in healthcare are chronicled, with particular attention to their anatomical integration and their expansive influence on medical science. In Chapter 14, the current overview of wearable healthcare technology delves into the physiological and biochemical intricacies of these devices, emphasizing their role in proactive health monitoring. In Chapter 15, techniques for real-time data acquisition and analysis are detailed, revealing their microbiological and immunological aspects and their revolutionary impact on health data collection. Chapter 16, based on real-time data applications in healthcare, discusses the groundbreaking implications for pharmacological interventions, heralding a new wave of data-informed treatment protocols. In Chapter 17, the strategies for the integration of wearable technology with healthcare systems are outlined, with a focus on the pathological considerations vital for successful device adoption. In Chapter 18, the challenges and solutions surrounding healthcare system integration are examined from a histological perspective, addressing the nuanced difficulties of incorporating wearable devices into medical routines. In Chapter 19, the wearable technology’s role in mental health monitoring and management is dissected, providing psychiatric insights into the adoption and efficacy of these innovative tools. In Chapter 20, the ethical considerations in mental health data collection and analysis are scrutinized, discussing the delicate balance between technology’s benefits and the privacy of the individuals. Next, Chapter 21 discusses the ethical challenges and guidelines necessary for the deployment of AI in healthcare, offering perspectives from urology and gastroenterology on how to navigate this complex landscape. Finally, Chapter 22 concludes the book, and a forward-looking perspective highlights the future directions and opportunities within AI-driven healthcare, inspiring family medicine and anaesthesiology to integrate AI in their practices.

To conclude, we extend heartfelt gratitude to all the authors who contributed their expertise to this volume. Their dedication to advancing our understanding and their commitment to ethical practice in Internet of Medicine and AI-driven healthcare sets the groundwork for the future of medicine. This book is dedicated to all those who stand at the forefront of technological innovation and healthcare, who seek to push the boundaries of what is possible for the benefit of patients worldwide.

Dr. Abhishek Kumar

Department of CSE, Chandigarh University, Mohali, India

Narayan Vyas

Department of Computer Science and Application, Vivekananda Global University, Jaipur, India

Dr. Pramod Singh Rathore

Department of Computer and Communication Engineering, Manipal University, Jaipur, India

Dr. Abhineed Anand

Professor & Dean Computer Science and Engineering, Bahara University, Waknaghat, Solan (HP), India

Pooja Dixit

Department of Computer Science, Sophia Girls’ College (Autonomous), Ajmer, India

1Omics Data Integration in AI System for Immediate and Carryover Effects of Neurodynamic Exercises on SLR Ranges Among Acute PIVD Patients

Durga Bahuguna, Vaibhav Agarwal* and Manish Kumar Jha

Department of Physiotherapy, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Jolly Grant, Dehradun, India

Abstract

Prolapsed interverterbral disc (PIVD) is a disease that occurs when there is a rupture of annulus fibrosus, which further leads to an effusion of nucleus purposes. In order to obtain a more thorough knowledge of a biological system, data from several omics technologies—such as transcriptomics, proteomics, metabolomics, and genomics—are combined in a process known as “omics data integration.” Thus, different medical data of each patient are collected, which makes easy for their treatment. The aim of this study is to analyze the immediate and carryover effects of neurodynamic exercises on straight leg raise test ranges among acute PIVD patients using omics data integration in artificial intelligence (AI) system. There is no evidence-based study on neurodynamic exercises including static opener and four levels of sliders and tensioners to decrease the mechanosensitivity and nerve root irritation among acute PIVD patients, to promote more tolerance to exercises with less pain experience, and to decrease more reliability over electrotherapy, using omics data integration in AI system. This study can be further carried by comparing the age and gender by giving two different interventions, and their effectiveness can be seen in longer period of time, which becomes easy due to AI.

Keywords: AI, data integration, SLR, neurodynamics, omics, PIVD

1.1 Introduction

Prolapsed interverterbral disc (PIVD) is a disease that occurs when there is a rupture of annulus fibrosus, which further leads to an effusion of nucleus pulposus [1, 2]. Lumbar disc herniation is a frequent condition that affects 5% of individuals in adults [3]. In the world, the incidence of PIVD in males is from 1.9% to 7.6%, and, in females, it is between 2.2% and 5.0% [4]. It is one of the most common condition among low back ache (LBA) patients, which affects about 10% of the population [5, 6]. It consists of four stages that are nucleus degeneration, nucleus displacement, protusion, and extrusion [7, 8].

Studies have been conducted in usefulness of various physical examination results. They suggest that the straight leg raise (SLR) continues to remains the gold standard test for identifying the radicular symptoms [9]. Root irritation is typically thought to be present when the examiner elevates the affected limb and the pain reproduces or intensifies [10–15]. When the test reproduces pain in the gluteal or lower leg region as the examiner passively lifts the affected leg with the hip in flexion and knee in extension, it is said to be positive. The relevance given to the angle of elevation at which the pain is produced varies greatly. Brieg and Troups and others suggested that less than 70° is clinically relevant [16–19].

PIVD propulsion of disc leads to compression due to which there is compromised distal sliding of neural structures, which, in turn, possess challenges to physiotherapist in treatment of acute PIVD patients. There is no evidence-based study on neurodynamic exercises including static opener and four levels of sliders and tensioners to decrease the mechanosensitivity and nerve root irritation among acute PIVD patients. The purpose of this study was to analyze that neurodynamic exercise including static opener and four levels of sliders and tensioners can significantly alter neural mobility, SLR ranges, visual analog scale (VAS), and pain side code score [20–26].

The primary focus in gaining practical understanding of cellular processes is now on the examination of multi-omics data in conjunction with clinical informations. A methodical and thorough comprehension of complex biology seems to be possible through the integrations of multi-omics data relevant to biomolecule at several levels. They aid in evaluating the information transfer between omics levels and, hence, aid in closing the gap between genotype and phenotype. Integrative techniques can eventually contribute to better prevention and management because they can research biological phenomena holistically, which can enhance prognostics and the accuracy with which disease phenotypes are predicted [27–32].

The Omics Discovery Index (OmicsDI; https://www.omicsdi.org/) is a common data format that holds data sets from eleven repositories. Access, exploration, and integration of proteomics, transcriptomics, metabolomics, and genomics datasets are facilitated by this open-source platform. Human, model, and non-model organism datasets are all included. For each data collection that may be merged, OmicsDI includes an annotation as well as normalization stage in addition to indexing the data sets [33–36].

Numerous illnesses, including cancer, exhibit heterogeneity due to the notable variations in the extent of cancer growth across afflicted people. Furthermore, a variety of other variables, including lifestyle and environment, may contribute to illness heterogeneity. Therefore, in order to fully comprehend the etiology of the disorder and choose appropriate therapies for patient belonging to distinct subtype, it is essential to identify the underlying subcategories of diseases or categorize samples into recognized subgroups. A number of methods are available that use multi-omics data from sample to determine disease subtype or categorize different samples according to their omics profile. This section covers the instruments used to comprehend sample subgrouping according to underlying molecular patterns [35].

1.2 Literature Review

A systemic review to analyze the effectiveness of exercises program after lumbar discectomy surgery, by Nafsika Atsidakou et al., in Journal of Clinical Orthopaedics and Trauma (2021): The current systemic review found that the included studies’ methodologically quality, as measured by PEDro, is regarded moderate. Exercise programs are advised for patients who have undergone lumbar discectomy because the results indicated that they improved pain, disability, quality of life, muscle strength, and ability to return to work [1].

Prolapsed, herniated, or extruded intervertebral disc treatment by only stabilization, by Atul Goel et al., in Journal of Craniovertebral Junction and Spine (2018): This study suggests that only fixation is also effective and safe in the management of lumbar herniated disc [2].

A case report and literature review: Spontaneous regression of a large sequestrated lumbar disc herniation, by Chengxiang Hu et al., in Journal of International Medical Research, SAGE (2021): In this study, it was concluded, that compared to individuals with protruding or bulging disc, those with the disc herniation subtype known as sequestration are substantially more likely to experience spontaneous regression, and it also suggested that, in the absence of clear-cut surgical indication, conservative treatment is preferred for individuals with a large herniated lumbar discs, particularly if it is of the extrusions or sequestration subtype [3].

A case report: Prolapsed lumbar interverterbral disease treatment through acupuncture, by Dr. S. M. Shahidul Islam et al., in Journal of Sports and Physical Education (2022): In this study, it was found that the patients having prolapsed lumbar intervertebral disc prolapse disease have significantly showed improvement in low back pain after doing exercises including lumbar mobilization, manipulation, isometric back muscle strengthening exercises, and acupuncture treatment in lumbar area [4].

A systemic “Review and meta-analysis: Efficacy of physiotherapy intervention in management of lumbar prolapsed intervertebral disc,” by Varun Singh et al., in International Journal of Health Sciences (2021): This study reveals that interventions in physiotherapy are successful in reducing pain and disability. Mechanisms involving physiology, biomechanics, and spinal mobility can be increased as a result of procedures including disc replacement foramina opening and intervertebral space expansion. This meta-analysis did not find any significant effects of physiotherapy on brain mobility [5].

A double blind randomized controlled trial: Effect of neurodynamic technique on radiating symptom and mechanosensitivity of neural tissue in subjects with lumbosacral radiculopathy, by Mohit Bipin et al., in International Journal of Medical Science and Public Health (2021). This study revealed that patients with lumbosacral radiculopathy, neurodynamic treatments are useful in inducing centralization, lowering the mechanosensitivity of the neural tissues and decreasing the bothersomeness and frequency of radiating symptoms [6].

Randomized Control Trial: “Effect of Mckenzie approach and Mulligan’s mobilization (SNAGS) in lumbar disc prolapse with unilateral radiculopathy,” by Trupti Warude et al., in International Journal of Science and Research (2014): In this study, the results showed that, in PIVD with unilateral radiculopathy, Mulligan’s mobilization (SNAGS) technique showed more significant improvement than Mckenzie approach in response to relieve pain, improving ROM and reducing functional disability [7].

Straight leg raise test, by Camino-Willhuber et al. (2019); The inter-observer reproducibility with low back pain in general practice, by Van den Hoogen et al., in Journal Starpearls (1996): In this study, it was found that, although the reproducibility of Lasegue’s sign in ordinary general practices appears to be modest, it may be comparable to that seen in hospital setting in patient who is specifically chosen because they have high likelihood of experiencing low back pain as a result of a particular condition [8].

Dejong’s The Neurologic examination, 7th edition, by William W. Campbell (ed.) South Asian edition, Chapter 47, page no 816: This book explains about the neurological examination, and one of its chapters explains about LBA examination [9].

“Passive straight leg raise test: Definition, interpretation, limitations and utilization spine health” (2014): According to this article, a true-positive SLR should replicate or worsen the pain/discomfort in the concerned limb at any level of passive elevation. False positives should be assumed to exist for those who only experience worsening back pain or any other leg pain outside the one that is the primary complaints [10].

Randomized Control Trial: “Difference between neurodynamic mobilization and streching exercises for chronic discogenic sciatica,” by Haytham I. Morsi et al., in Medical Journal of Cairo University (2021): This study reveals that, in the treatment of individuals with persistent discogenic sciatica, slider and tensioner neurodynamic mobilization techniques are more efficient than stretching exercise in decreasing pain intensity and enhancing functional impairment. Furthermore, there are no discernible differences between the two methods for increasing ankle dorsiflexion ROM [11].

“A prospective RCT: Effectiveness of nerve flossing technique in chronic lumbar radiculopathy,” by Bhatia Sweta Satishkumar et al., in Indian Journal of Physiotherapy and Occupational Therapy (2017): In this study, it was found that nerve flossing technique can be utilised as adjuvant therapy to help patients with chronic lumbar radiculopathy because of intervertebral disc prolapse to reduce pain, improve hip range of motion, and decrease functional impairment and fear avoidance beliefs (FABQ-G) [12]. A cross sectional study: The prevalence and correlates of low back pain in adults in Southern-Indian population, by Anil Chankaramangalam Mathew et al., in International Journal of Medicine and Public Health (2013): According to this study, low back pain affects 40.7% of respondents who are years of age or older. In comparison to males (28.14%), women (52.9%) had a much greater prevalence, and this difference was statistically significant [13].

“A double-blind randomized controlled trial: The effect of spinal mobilization with leg movement in patients with lumbar radiculopathy,” by Kiran Satpute et al., in Journal Archives of Physical Medicine and Rehabilitation (2019): In this study, it was found that the addition of spinal mobilization with leg movement to exercise and TENS dramatically increased benefits in leg and lower back ache, disability, SLR, and ROM, for patient with subacute lumbar radiculopathy in the short and long duration [14].

A randomized control trial: “Comparison of the effectiveness of core strengthening exercises and Mckenzie extension exercise on the pain functional disability in lumbar PIVD condition,” by Jyoti Sharma et al., in Physiotherapy and Occupational Therapy Journal (2018): This study demonstrated the great effectiveness of the Mckenzie extension exercise in term of reducing pain and improving functional disability in LBA patient having prolapsed lumbar intervertebral disc conditions. It also revealed that, while the Mckenzie exercise program assists to reduce disc prolapse, the core stabilization exercises assist in strengthening the surrounding muscles to provide stability [15].

A case report: “Otago home exercise program along with other physiotherapy interventions for the management of prolapsed intervertebral disc and associated symptoms in an elderly,” by Sneha Chakraverty et al., in Journal of Bulletin of Faculty of Physical Therapy (2023): This case study stated that the physical therapy and symptomatic management intervention had a positive effect on the patient, which led to decreased discomfort, increased muscle power and lower possibility of falling. The Otago home exercise program combined with physical therapy can be advantageous for old age cases to improve balance and possibility of falls and for the management of prolapsed intervertebral disc [16].

Cross-sectional study: “Demographic profile of the patients with lumbar disc herniation presented in a tertiary care centre,” by Jungindro Singh Ningthoujam et al., in Journal of Medical Society (2023): This study suggest that disc herniation is more prevalent in middle-aged group females. People with high BMI or overweight are more prone to disc prolapse, though it is not statistically significant. The levels of prolapse most frequently seen are L4-L5 and L5-S1. Left side and posterolateral disc prolapse is more frequent [17].

A randomized control trial: “Effect of neurodynamic mobilization on pain and function in subjects with lumbo-sacral radiculopathy,” by Srishti Sanat Sharma et al., in International Medical Journal (2018): This study suggest that neurodynamic treatment is advantageous for reducing pain (during activity not at rest) and improving function but hotpack, core and back isometric exercises only showed improvement in pain but not in function [18].

Indhupriya Subramanian et al. conducted a research, in which it is essential to use an integrative strategy that integrates multi-omics data to emphasize the links between the many biomolecule implicated and their roles in order to examine complicated biological processes coherently. The tools and techniques that use integrative approaches to evaluate numerous omics data were gathered for this study, and their capacity to handle applications such illness subtyping, biomarker prediction, and extracting insights from the data was summed up. They offer these tools’ limits, usecases, and approach; visualization portals; and the difficulties in integrating multi-omics data [35].

N. Shusharina et al. highlight the interesting directions in the study of machine learning application for the treatment and prevention of depression and neurodegenerative diseases. Finding novel method for diagnosing and treating depressive disorders and neurodegenerative illnesses continue to be a top focus for research in genetics, neurophysiology, psychology, and multidisciplinary medicine, even after decades of study. Before the improvements are extensively implemented in clinics, there is still work to be done to come to an agreement on how to apply the new machine learning techniques and integrate them with the present standards of care and assessments [36].

1.3 Methodology

1.3.1 Research Question

Are the neurodynamic exercises including static opener and four levels of neurodynamic can significantly alter neural mobility, SLR ranges, VAS, and pain site score (PSC) by using omics data integration?

1.3.2 Aim of the Study

The aim of this study is to analyze the immediate and carryover effect of neurodynamic exercises on SLR ranges among acute PIVD patients using omics data integration in artificial intelligence (AI) system [15].

1.3.3 Need of the Study

There is no evidence-based study on neurodynamic exercises including static opener and 4 levels of sliders and tensioners to decrease the mechanosensitivity and nerve root irritation among acute PIVD patients, to promote more tolerance to exercises with less pain experience, and to decrease more reliability over electrotherapy, using omics data integration in AI system; to gather research based evidences to neurodynamic approach using AI in acute PIVD patients; and to explore rehabilitation options and strategies in managing acute PIVD patients using AI systems.

1.4 Hypothesis

1.4.1 Alternate Hypothesis

Neurodynamic treatment shows more significant improvement and SLR ranges among acute PIVD patients as compare to control group.

1.4.2 Null Hypothesis

Neurodynamic treatment may not show more significant improvement in SLR ranges among acute PIVD patients as compare to control group.

1.4.3 Study Design

The design of this study was double-blinded experimental study.

1.4.4 Study Center

The study was conducted in Neuromedicine Ward and Physiotherapy OPD, Himalayan Hospital Jolly Grant, Dehradun, Uttarakhand.

1.4.5 Study Population

All subjects were taken from Himalayan Hospital diagnosed with PIVD were considered as study population.

1.4.6 Sampling

Random sampling method

1.4.7 Study Duration

Nine months

1.4.8 Sample Size

Forty-four subjects are selected according to the inclusion and exclusion criteria.

1.4.9 Inclusion Criteria

Acute PIVD patients (less than 3 months) with confirmed diagnosis with MRI.

Unilateral radiculopathy

SLR ranges less than 70°

Age group between 20 and 60 years

1.4.10 Exclusion Criteria

Myotomal involvement power less than 3

Low back surgery in past 1 year

Spondylolisthesis, spinal canal stenosis, SI joint pain, infection, tumor, and cauda equina syndrome

1.4.11 Outcome Variables

Passive (SLR) test

VAS score

Pain site code (PSC)

1.4.12 Instrumentation

Universal goniometer, couch, pillow, and stopwatch. Figures 1.1 and 1.2 shows the Universal gonimeter and stopwatch respectively.

Figure 1.1 Universal goniometer.

Figure 1.2 Stopwatch.

1.5 Procedure

Subjects meeting the inclusion and exclusion criteria were included in the study.

Subject were divided into two groups:

Experimental groups

Control groups

After the collection of the demographic details, subjects were tested for VAS, PSC, and SLR. Figure 1.3 shows the testing of SLR and Figure 1.4 shows the measuring of SLR range.

The subject following the inclusion criteria are included in the study.

Pre- and post-assessment was done by another therapist, and the intervention was given by the researcher.

Before screening, the subjects have received a thorough description of the study’s objective.

The experimental group received a neurodynamic intervention (static opener and four levels of neurodynamics).

A total of three sets of 15–20 repetitions of four levels of neurodynamics with 1-min static opener were given to the experimental group including periods of rest (15–20 s).

Figure 1.3 Testing of SLR.

Figure 1.4 Measuring of SLR range.

1.6 Intervention

1.6.1 Four Levels of Neurodynamics

Position: Supine lying

Level 1: Position Away      Move Away

Passive Neck flexion along with hip and knee flexion

Level 2: Position Toward     Move Away

Neck extension along with hip and knee flexion

Level 3: Position Away      Move Toward

Neck flexion along with hip and knee extension followed by

C/L     SLR     B/L SLR     Affected SLR

Level 4: Position toward     Move Toward

Neck extension along with hip and knee extension followed by

C/L SLR     B/L SLR       Affected SLR.

1.6.2 Control Group Intervention

Spinal extension exercise

Core isometric exercise

Glutes isometric exercise

Pelvic bridging exercise

A total of three sets of 15 repetitions of each exercise were performed including period of rest (10–15 s).

1.7 Data Analysis

Shapiro–Wilk test was performed to check the normality of the data. In the control group, only Pre-VAS and Pre-SLR were following normality in the data. Wilcoxon signed-rank test was used to find out the difference of outcome variable scores within groups for VAS, PSC score, and SLR. Paired t-test was used to find out the difference of VAS and SLR variable in the control group as it was following normality.

1.8 Result

A total of 44 patients are included in the study. From this patient, 22 patients were enrolled in the experimental group and 22 in control group. Among them, 20 were male and 24 were female. Two patients were dropped out from the experimental group during carryover effect period. Table 1.1 and Figure 1.5 shows the mean and Standard deviation of Age, sex and side.

Table 1.1 Mean age, sex, and side.

Variable

N

Mean

Standard deviation

Age

44

39.14

9.833

Sex

44

1.55

0.510

Side

44

1.50

0.521

Figure 1.5 Mean age, sex, and side in both groups (baseline data for demographic details).

Table 1.2 Mean and standard deviation of age in both groups.

SN

Variable

N

Experimental group

Control group

1

Age

44

37.23 ± 9.783

41.05 ± 9.727

Mean age in the data was 39.14 ± 9.833.

Mean sex in the data was 1.55 ± 0.510.

Mean side in the data was 1.50 ± 0.521.

The percentage of males and females in the data was 45.5% and 54.5%, respectively.

Data are mean ± standard deviation (sd). In the experimental group, the mean age is 37.23 and sd is 9.783, and, in the control group, the mean age is 41.05 and sd is 9.727. Table 1.2 shows the mean ans standard deviation of age in both experimental and control group.

Wilcoxon signed-rank test was used to compare the outcome variable score in the experimental group. In the experimental group, the VAS score was reduced from 5.95 with standard deviation of 0.844 to post VAS score of 3.68 with standard deviation of 1.129, which was statistically significant (p-value < 0.001). The PSC score was reduced from 5.36 with standard deviation of 0.790 to post PSC score of 3.73 with standard deviation of 1.386, which was statistically significant (p value <0.001). The SLR score was increased from 49.45 with standard deviation of 9.635 to post SLR score of 58.18 with standard deviation of 14.608 which was statistically significant (p-value: 0.002). Table 1.3 and Figure 1.6 shows the pre and post data within the experimental group.

In the experimental group, the post VAS score was reduced from 3.68 with standard deviation of 1.129 to carry over VAS score of 2.60 with standard deviation of 1.465, which was statistically significant (p-value: 0.002). The post PSC score was reduced from 3.733 with standard deviation of 1.386 to carry over PSC score of 3.05 with standard deviation of 1.395, which was statistically significant (p-value: 0.032). The post SLR score was increased from 58.18 with standard deviation of 14.608 to carry over SLR score from 70.70 with standard deviation of 10.623, which was statistically significant (p-value < 0.001). Table 1.4 and Figure 1.7 shows the post and carryover data withoion the experimental group.

Table 1.3 Pre-post data within the experimental group.

SN no.

Variables

Pre Mean ± sd (Median)

Post Mean ± sd (Median)

P-Value

1

VAS

5.95 ± 0.844 (6.000)

3.68 ± 1.129 (4.000)

<0.001

2

PSC

5.36 ± 0.790 (5.500)

3.73 ± 1.386 (3.500)

<0.001

3

SLR

49.45 ± 9.635 (54.000)

58.18 ± 14.608 (63.000)

0.002

Figure 1.6 Pre-post data within the experimental group.

Table 1.4 Post and carryover data within the experimental group.

SN no.

Variables

Post mean ± sd (median)

Carryover mean ± sd (median)

P-Value

1

VAS

3.68 ± 1.129 (4.000)

2.60 ± 1.465 (2.500)

0.002

2

PSC

3.73 ± 1.386 (3.500)

3.05 ± 1.395 (2.500)

0.032

3

SLR

58.18 ± 14.608 (63.000)

70.70 ± 10.623 (72.000)

<0.001

Figure 1.7 Post-carryover data within the experimental group.

Table 1.5 Pre-post data within the control group.

SN no.

Variables

Pre mean ± sd (Median)

Post mean ± sd (Median)

P-value

1

VAS

6.14 ± 1.283 (6.000)

4.73 ± 1.667 (5.000)

<0.001

2

PSC

5.32 ± 0.780 (5.500)

4.55 ± 1.335 (5.000)

0.007

3

SLR

48.73 ± 8.708 (49.000)

54.32 ± 11.623 (55.000)

0.007

In the control group, the VAS score was reduced from 6.14 with standard deviation of 1.283 to post VAS score of 4.73 with standard deviation of 1.667, which was statistically significant (p-value < 0.001). The PSC score was reduced from 5.32 with standard deviation of 0.780 to post PSC score of 4.55 with standard deviation of 1.335, which was statistically not significant (p-value: 0.007). The SLR score was increased from 48.73 with standard deviation of 8.708 to post SLR score of 54.32 with standard deviation of 11.623, which was statistically significant (p-value: 0.007). Table 1.5 shows the pre and post data within the control group.

1.9 Discussion

This study analyzed and compared the immediate and carryover effects of neurodynamics in SLR ranges among acute PIVD patients. The purpose of this study was to evaluate that neurodynamic exercises including static opener and four levels of sliders and tensioners can significantly alter neural mobility, SLR ranges, VAS, and PSC score using omics data.

Gaston Camino-Willhuber et al. (2019) suggested that neurodynamics helps in reducing pain by developing centralization [8]. Ali M. Alshami et al. (2021) conducted a study in which he found that patients receiving sliders and tensioners showed significant improvement in pain [31]. In a study, Dharti Hingarajia et al. (2019) found that sliders neural mobilization exercises are efficacious in reducing pain immediately [32]. The PSC score was statistically reduced when we compared the pre-post data (p-value < 0.001) and the post-carryover data (p-value: 0.032). Mohit Bipin Shah et al. (2021) conducted a research in which he found that the radiating symptoms may be centralized using neurodynamics [6]. In present study, the SLR range was increased when we compared the pre-post data (p-value: 0.002) and the post-carryover data, which was statistically significant (p-value < 0.001). Neha Tambekar et al. (2015) conducted a study in which the butler’s neural mobilization showed significant improvement in SLR ranges immediately after the session, but it was not maintained for 24 h [21]. Muhammad Adnan et al. (2012) suggested that neurodynamic treatment is effective in improving SLR ranges in patients having radiating LBA [22].

When the data was analyzed in the control group, it was found that the VAS score was statistically reduced when the pre-post data was compared (p-value < 0.001), but it was not maintained for long time when we compared the post-carryover data (p-value: 0.424). Jyoti Sharma et al. (2018) advised that Mckenzie extension exercises were efficacious in decreasing lower back pain in patients with prolapsed intervertebral disc [16]. The PSC score was reduced when we compared the pre-post data (p-value < 0.007) but not in the post-carryover data (p-value: 0.0340). With the best of my knowledge, this might be because the VAS was reduced, which can alter the sensitivity of pain so the patient had experience the improvement in radiating pain. It was not maintained in carryover effect due to the reason that the intervention was given only for one session, but, if we will give the intervention in continuation for 1 week, then it might show improvement in PSC score. Jyoti Sharma et al. (2018) advised that Mckenzie extension exercises yield results in 3–4 days, whereas core strengthening exercises yield results in 10 days [16]. Deepak Anap et al. (2015) studied a case series in which they suggested that core strengthening exercises are useful in reducing nerve root irritation and decreasing the radiating pain [34]. In present study, the SLR range was increased when we compared the pre-post data (p-value < 0.007) but not in the post-carryover data (p-value: 0.388). This indicates that the core strengthening exercises can reduce the pain immediately due to which the patient will get relief in radiating pain as well as the SLR range will increase but effect will be for short period of time as compared AI-based devices.