Diagnosing Musculoskeletal Conditions using Artificial Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging

1st Edition - November 16, 2024
Imprint: Academic Press
Editors: Rakesh Kumar, Meenu Gupta, Ajith Abraham, Paul Antony
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 8 9 2 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 8 9 3 - 0

Bone deformations can lead to musculoskeletal disorders and negatively impact on individual quality of life. Early and accurate detection of bone deformation is crucial for… Read more

Diagnosing Musculoskeletal Conditions using Artificial Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging

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Bone deformations can lead to musculoskeletal disorders and negatively impact on individual quality of life. Early and accurate detection of bone deformation is crucial for effective medical intervention. Diagnosing Musculoskeletal Conditions Using Artificial Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging outlines a comprehensive approach that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques to identify bone deformations promptly and accurately. By leveraging advanced image analysis and pattern recognition, this approach aims to revolutionize the field of orthopedic diagnostics. This book covers challenges, technologies, applications, and future trends of AI and ML in Bone Deformation, addressing advanced and innovative techniques, frameworks, methodologies, and practical implementations of machine learning to get early predictions of Bone deformation. Written by experts in the field for researchers, surgeons, students, and instructors interested in musculoskeletal conditions.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter 1. An introductory approach to bone deformities, osteoarthrosis, osteoporosis, and spondylosis of spine using machine learning
1.1 Introduction
1.2 Review process
1.3 Literature survey
1.3.1 Identification of bone deformity
1.3.2 Identification of osteoarthrosis
1.3.3 Detection of osteoporosis
1.3.4 Identification of spondylosis
1.4 Proposed investigations
1.5 Methodology
1.6 Discussion
1.7 Conclusion
Chapter 2. Identification and classification of musculoskeletal conditions using artificial intelligence and machine learning
2.1 Introduction
2.2 Artificial intelligence and machine learning in health care
2.2.1 Basics of artificial intelligence and machine learning
2.2.2 Artificial intelligence and machine learning in medical imaging and diagnostics
2.3 Artificial intelligence–driven diagnostics in musculoskeletal condition treatment
2.4 Personalized musculoskeletal disorder treatment plans through artificial intelligence
2.4.1 Data analysis for personalized patient care
2.4.2 Predictive modeling for treatment strategies
2.5 Surgical applications of artificial intelligence in orthopedics
2.6 Challenges and ethical considerations
2.7 Conclusion
Chapter 3. Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning
3.1 Introduction
3.1.1 Bridging the gap: Health care meets technology
3.2 The crippling cost: Osteoarthritis and global health
3.2.1 Diagnosing osteoarthritis: The achilles' heel of traditional methods
3.2.1.1 Unreliable reliance on subjective assessments
3.2.1.2 The lag in detection with standard imaging
3.3 Potential of machine learning for early osteoarthritis detection
3.3.1 Machine learning: Reshaping the health care landscape
3.3.2 Spectrum of solutions: Diverse machine learning algorithms for early osteoarthritis diagnosis
3.3.3 Data drives the engine: Building accurate models
3.3.3.1 Harnessing the power of comprehensive datasets
3.3.3.2 Fine-tuning the machine: Feature selection and preprocessing
3.4 Ethical considerations in machine learning–based osteoarthritis diagnosis
3.4.1 Protecting our data: Navigating privacy concerns in healthcare artificial intelligence
3.4.2 Beyond the black box: Ensuring interpretability for trustworthy decisions
3.4.3 Charting a course: Strategies to mitigate ethical challenges
3.5 Real-world applications and benefits of machine learning for osteoarthritis detection
3.5.1 From theory to practice: Using machine learning for early diagnosis of hip and knee osteoarthritis
3.5.2 Personalized paths to care: Optimizing outcomes with machine learning–powered medicine
3.5.3 A ripple effect: The economic and societal benefits of early intervention
3.6 Future directions and research opportunities
3.6.1 Unveiling the future: Promising areas for machine learning–based osteoarthritis detection
3.6.2 Evolving technology, evolving medicine: Advancing algorithms, data acquisition, and validation
3.6.3 Building the bridge: Collaboration among researchers, clinicians, and tech developers
3.7 Conclusion
Chapter 4. Bone cancer classification and detection using machine learning technique
4.1 Introduction
4.2 Predictive modeling and prognostic assessment
4.2.1 Challenges and considerations
4.2.2 Scope of this chapter
4.3 Medical imaging modalities for bone cancer diagnosis
4.3.1 Machine learning algorithms for bone cancer classification
4.3.2 Challenges in bone cancer classification using machine learning
4.3.2.1 Data imbalance and rarity of bone cancer types
4.3.2.2 Complexity and heterogeneity of imaging data
4.3.2.3 Feature selection and extraction
4.3.2.4 Interpretability of machine learning models
4.3.2.5 Overfitting and generalization
4.3.2.6 Ethical and regulatory considerations
4.4 Feature selection and model interpretability
4.4.1 Feature selection
4.4.2 Model interpretability
4.4.3 Integrating machine learning models into clinical decision-making
4.4.3.1 Data quality and validation
4.4.3.2 Clinical relevance and interpretability
4.4.3.3 User-friendly interfaces and workflow integration
4.4.3.4 Ethical considerations and regulatory compliance
4.4.3.5 Education and collaboration
4.4.3.6 Clinical validation and real-world impact
4.5 Improving diagnostic accuracy and treatment planning
4.6 Prognostic assessments and patient outcomes prediction
4.7 Case study: Enhancing bone cancer diagnosis using machine learning
4.7.1 Methodology
4.7.2 Results
4.8 Conclusion
4.9 Future scope
Chapter 5. Identification of the risk of osteoporosis in older Vietnamese women using artificial intelligence and machine learning
5.1 Introduction
5.1.1 Background and importance of osteoporosis as a public health concern
5.1.2 The importance of employing precise prediction techniques in the evaluation of osteoporosis
5.2 Materials and methods
5.2.1 Data source: Hanoi Medical University Hospital
5.2.2 Consideration of potential risk factors (demographics, lifestyle, medical history, and blood tests)
5.2.3 Using data from the osteoporosis self-assessment tool
5.3 Machine learning models
5.4 Significance of the study
5.4.1 Contributions to osteoporosis screening techniques
5.4.2 Implications for Vietnam's health care system and policy development
5.5 Measurement
5.6 Results
5.7 Model development
5.8 Osteoporosis prediction in each machine learning model
5.9 Conclusion
Chapter 6. Identification of paget disease in the human body using artificial intelligence and machine learning
6.1 Introduction
6.2 Literature survey
6.3 Pathophysiology and remodeling of bones
6.4 Clinical presentation and causes
6.5 Diagnostic and screening methods
6.6 Complications associated with Paget disease
6.7 Current challenges in diagnosis
6.8 Potential of artificial intelligence and machine learning in medical imaging
6.9 Conclusion
Chapter 7. Identification and classification of rheumatoid arthritis using artificial intelligence and machine learning
7.1 Introduction
7.2 Rheumatoid arthritis
7.3 Fundamentals of artificial intelligence for rheumatoid arthritis analysis
7.3.1 Common artificial intelligence algorithms
7.3.2 Privacy and security considerations in artificial intelligence for rheumatoid arthritis
7.4 Machine learning in rheumatoid arthritis
7.4.1 Machine learning approaches and their applications in rheumatoid arthritis
7.4.2 Machine learning for advanced rheumatoid arthritis management
7.4.3 Different data types
7.4.3.1 Imaging data
7.4.3.2 Clinical and sensor data
7.4.3.3 Identifying phenotypes with electronic health records
7.4.4 Available datasets for research
7.4.5 Cloud computing for machine learning in rheumatoid arthritis
7.4.6 Applications of artificial intelligence in rheumatoid arthritis
7.5 Detection and identification of rheumatoid arthritis
7.6 Machine learning for rheumatoid arthritis classification
7.7 Assessing rheumatoid arthritis severity
7.8 Prediction of rheumatoid arthritis
7.9 Precision medicine and treatment response
7.10 Predicting rheumatoid arthritis prognosis
7.11 Drug discovery
7.12 Machine learning limitations and challenges
7.13 Future directions
7.14 Conclusion
Chapter 8. Early detection approach for analysis of osteoarthritis using artificial intelligence and machine learning
8.1 Introduction
8.2 Osteoarthritis detection methods using artificial intelligence and machine learning
8.3 Artificial intelligence–aided model
8.4 Artificial intelligence preventive solution for osteoarthritis
8.5 Machine learning preventive solution for osteoarthritis
8.6 Early detection of osteoarthritis using artificial intelligence and machine learning
8.7 Case studies of osteoarthritis using artificial intelligence and machine learning
8.7.1 Case study 1: Early detection of osteoarthritis through imaging analysis
8.7.2 Case study 2: Use of machine learning algorithms in creating personalized treatment plans
8.7.3 Case study 3: With artificial intelligence predicting osteoarthritis progression
8.7.4 Case study 4: Enhancing patient monitoring through machine learning and wearable devices
8.8 Challenges and future directions
Chapter 9. Preanalysis of ankylosing spondylitis using machine learning
9.1 Introduction
9.2 Related work
9.3 Preanalysis
9.4 Challenges faced
9.4.1 Data availability and quality
9.4.2 Feature engineering and selection
9.4.3 Interpretability of model
9.4.4 Ethics and bias
9.4.5 Regulatory compliance
9.4.6 Interdisciplinary collaboration
9.4.7 Computational resources
9.4.8 Continuous learning and adaptation
9.5 Strategies to address challenges
9.6 Conclusion
Chapter 10. Analysis and identification of gout flares using machine learning
10.1 Introduction
10.1.1 How natural language processing and machine learning are used to forecast gout flares
10.1.2 Problems of detecting gout owing to lack of diagnosis codes
10.1.3 The current medical code system and its limitations for gout flares
10.1.4 Significance of early identification for focused interventions and sustained joint health
10.2 Automatic recognition of gout attacks
10.2.1 Using machine learning and natural language processing to identify gout flares in automated clinical notes
10.2.2 Developing an analytical paradigm with machine learning algorithms
10.3 Early identification of severe gout
10.4 Models for predicting risk of hospitalization from gout flare
10.4.1 Development of hospitalization risk prediction models for patients with gout
10.4.2 Participation of patients with gout in the classification process
10.5 Conclusion
10.5.1 Summary of main conclusions and their implications for further study
10.5.2 Supplementary suggestions for managing and predicting gout flares
Chapter 11. Impact of amyloidosis on bones and its relationship to dementia
11.1 Amyloidosis: A brief overview
11.1.1 Systemic amyloidosis
11.1.2 Localized amyloidosis
11.1.3 Diagnosing localized amyloidosis
11.1.3.1 Kidney
11.1.3.2 Cardiovascular system
11.1.3.3 Respiratory tract
11.1.3.4 Alimentary tract
11.1.3.5 Liver and spleen
11.1.3.6 Nervous system
11.2 Amyloidosis and bones
11.2.1 Microscopic appearance of amyloid
11.2.2 Bones, joints, and tendons
11.3 Prediction of amyloidosis using artificial intelligence and machine learning techniques
11.3.1 Algorithm to develop a model to predict amyloidosis
11.3.2 Working procedure
11.3.2.1 Features extraction
11.3.2.2 Data processing
11.3.2.3 Validation techniques
11.3.2.4 Algorithm design for training
11.3.2.5 Metrics for evaluating results
11.3.3 Methods for detecting amyloidosis
11.3.3.1 Naive Bayesian classification
11.3.3.2 Decision tree
11.4 Artificial intelligence and machine learning–based approach to amyloidosis detection model
11.5 Amyloidosis and dementia
11.5.1 Amyloidosis and dementia: Key definitions and mechanisms
11.5.2 Literature survey linking amyloidosis and dementia
11.5.3 Other treatments, methods, and diagnostics
11.5.4 The amyloid clock
11.6 Conclusion
Chapter 12. Machine learning for bone deformation detection in real-world applications
12.1 Introduction
12.2 Classic issues in bone deformation analysis
12.2.1 Historical context and key challenges faced
12.3 Importance of precision and efficiency in diagnosis
12.3.1 Precision diagnosis
12.3.2 Impact on treatment planning
12.3.3 Early detection and prevention
12.3.4 Diagnostic efficiency
12.3.5 Technological advancements and precision
12.4 Machine learning solutions for bone deformation
12.4.1 Leveraging large datasets for pattern recognition
12.4.1.1 Comprehensive data insights
12.4.1.2 Improved early detection
12.4.1.3 Customized treatment strategies
12.4.1.4 Challenges and opportunities
12.4.2 Deep dive into machine learning techniques: Deep learning, support vector machine, and ensemble methods
12.4.2.1 Deep learning: Revealing intricate patterns
12.4.2.2 Support vector machine
12.4.2.3 Ensemble methods: Improving precision and dependability
12.4.2.4 Integration of machine learning techniques
12.4.2.5 Clinical uses and future prospects
12.5 Deep learning for bone deformations
12.5.1 Understanding convolutional neural networks for image analysis
12.5.1.1 Fully connected layers
12.5.1.2 Utilization in bone deformation analysis
12.5.1.3 Recurrent neural networks for temporal data in deformation tracking
12.5.1.4 Managing time-series data
12.5.1.5 Understanding temporal dependencies
12.5.1.6 Predictive modeling
12.5.1.7 Treatment monitoring and decision support
12.6 Support vector machine in bone deformation
12.6.1 Support vector machine for classification of bone irregularities
12.6.1.1 Support vector machine principles
12.6.1.2 Kernel trick for nonlinear classification
12.6.1.3 Feature selection and dimensionality reduction
12.6.1.4 Medical uses and advantages
12.7 Ensemble methods for improved accuracy
12.7.1 Ensemble learning strategies in bone deformation identification
12.7.1.1 AdaBoost for improved classification
12.7.2 Advantages and challenges of ensemble techniques
12.7.3 Challenges in ensemble techniques
12.8 Ethical, practical, and technical considerations
12.8.1 Privacy protection measures in health care data handling
12.8.2 Interpretability of machine learning models for clinical decision-making
12.8.3 Importance of interdisciplinary collaboration in medical artificial intelligence
12.9 Case study
12.9.1 Background
12.9.2 Introduction
12.9.3 Diagnosis
12.9.4 Application of machine learning
12.9.5 Result
12.9.6 Conclusion
12.10 Conclusion
12.11 Transforming bone deformation diagnosis
12.11.1 Recap of machine learning's role in improving clinical assessments
12.11.2 Future prospects and emerging trends in medical artificial intelligence
12.11.3 Inspiring further research and collaboration opportunities
Chapter 13. Artificial intelligence and machine learning for foot and ankle disorders
13.1 Introduction
13.1.1 Health care monitoring
13.2 Machine learning in health care
13.3 Artificial intelligence and machine learning for foot and ankle disorders
13.4 Enhancing the accuracy of diagnosing foot disorders using artificial intelligence
13.5 Role of artificial intelligence in foot disorder diagnostics
13.6 Applications of artificial intelligence and machine learning in foot and ankle disorders
13.7 Benefits of artificial intelligence and machine learning in foot and ankle care
13.8 Rehabilitation and monitoring of recovery in foot and ankle disorders
13.9 Diagnostic tools and techniques in foot and ankle disorders
13.10 Case studies
13.10.1 Case studies of artificial intelligence in diagnosis of foot and ankle disorders
13.10.2 Case studies and Clinical Trials on successful implementations of artificial intelligence in diagnosing foot and ankle disorders
13.11 Real-world examples of artificial intelligence implementations in diagnosing foot and ankle disorders
13.12 Future directions and emerging trends in artificial intelligence and machine learning for foot and ankle disorders
13.13 Conclusion
Chapter 14. Conclusion: A future perspective on diagnosing musculoskeletal conditions using artificial intelligence and machine learning
14.1 Introduction
14.2 Various musculoskeletal disorders
14.2.1 Neck and shoulders
14.2.2 Osteoarthritis
14.2.3 Osteoporosis
14.2.4 Fragility of bones
14.2.5 Fractures
14.2.6 Determination of bone age
14.3 Spine imaging
14.3.1 X-rays
14.3.2 Computed tomography
14.3.3 Magnetic resonance imaging
14.3.4 Functional magnetic resonance imaging
14.3.5 Positron emission tomography
14.4 Musculoskeletal diagnosis
14.5 Musculoskeletal oncology
14.6 Detection of anterior cruciate ligament and meniscal structures
14.7 Spinal structure
14.8 Other potential pediatric musculoskeletal applications
14.8.1 Hip dysplasia during development
14.9 Technical aspects
14.9.1 Artificial intelligence/machine learning and deep learning
14.9.2 Other artificial intelligence–based tools for musculoskeletal diagnosis
14.10 Discussion and conclusion
Index

Rakesh Kumar

Dr. Rakesh Kumar is working as a Professor in the Department of Computer Science Engineering at Chandigarh University, Punjab, India. He did his Ph.D. from Punjab Technical University, Jalandhar, in Computer Science and Engineering in 2017. He has published many authored books with reputed publishers. His research interests are IoT, Machine Learning, and Natural Language Processing. A total of 19+ Years of Academic / Research Experience with more than 100 Publications in various National/International Conferences and Journals. He also organized three international conferences under the banner of IEEE Explore and AIP publisher.

Affiliations and expertise

Professor, Chandigarh University

Meenu Gupta

Dr. Meenu Gupta is an Associate Professor at the UIE-CSE Department, Chandigarh University, India. She completed her Ph.D. in Computer Science and Engineering with an emphasis on Traffic Accident Severity Problems from Ansal University, Gurgaon, India, in 2020. She has more than 15 years of teaching experience. Her research areas cover Machine Learning, Intelligent Systems, and Data mining, with a specific interest in Artificial Intelligence, Image Processing and Analysis, Smart Cities, Data Analysis, and Human/Brain-machine Interaction (BMI). She has five edited and four authored books. She has also authored or co-authored more than 20 book chapters and over 80 papers in refereed international journals and conferences. She has five filled patents and was awarded the best faculty and department researcher in 2021 and 2022.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering University Centre of Research and Development Chandigarh University, Punjab, India

Ajith Abraham

Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.

Affiliations and expertise

Pro Vice-Chancellor, Sai University, Chennai, India

Paul Antony

Dr. Paul Antony is working as Senior and Lead consultant in Department of Orthopaedics at Apollo Super speciality Hospital Muscat, Oman. He did his postgraduate degree from Christian Medical college, Ludhiana from Panjab University in 1998 and did his SICOT & AO fellowship in shoulder and knee surgeries from Germany . He has more than 24 years experience in the field of arthroplasty and arthroscopic surgeries of shoulder, knee and hip joints.

Affiliations and expertise

Sr Consultant, Dept of Orthopaedics, Apollo Super Specialty Hospital, India

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Diagnosing Musculoskeletal Conditions using Artificial Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging

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