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.
1. Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging - p. iii 2. Cover - p. iii 3. Title page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xvii 7. Preface - p. xxi 8. Chapter 1 An introductory approach to bone deformities, osteoarthrosis, osteoporosis, and spondylosis of spine using machine learning - p. 2 9. Chapter 2 Identification and classification of musculoskeletal conditions using artificial intelligence and machine learning - p. 22 10. Chapter 3 Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning - p. 40 11. Chapter 4 Bone cancer classification and detection using machine learning technique - p. 66 12. Chapter 5 Identification of the risk of osteoporosis in older Vietnamese women using artificial intelligence and machine learning - p. 82 13. Chapter 6 Identification of paget disease in the human body using artificial intelligence and machine learning - p. 106 14. Chapter 7 Identification and classification of rheumatoid arthritis using artificial intelligence and machine learning - p. 124 15. Chapter 8 Early detection approach for analysis of osteoarthritis using artificial intelligence and machine learning - p. 148 16. Chapter 9 Preanalysis of ankylosing spondylitis using machine learning - p. 168 17. Chapter 10 Analysis and identification of gout flares using machine learning - p. 180 18. Chapter 11 Impact of amyloidosis on bones and its relationship to dementia - p. 196 19. Chapter 12 Machine learning for bone deformation detection in real-world applications - p. 224 20. Chapter 13 Artificial intelligence and machine learning for foot and ankle disorders - p. 244 21. Chapter 14 Conclusion: A future perspective on diagnosing musculoskeletal conditions using artificial intelligence and machine learning - p. 268 22. Index - p. 285
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.
1. Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging - p. iii 2. Cover - p. iii 3. Title page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xvii 7. Preface - p. xxi 8. Chapter 1 An introductory approach to bone deformities, osteoarthrosis, osteoporosis, and spondylosis of spine using machine learning - p. 2 9. Chapter 2 Identification and classification of musculoskeletal conditions using artificial intelligence and machine learning - p. 22 10. Chapter 3 Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning - p. 40 11. Chapter 4 Bone cancer classification and detection using machine learning technique - p. 66 12. Chapter 5 Identification of the risk of osteoporosis in older Vietnamese women using artificial intelligence and machine learning - p. 82 13. Chapter 6 Identification of paget disease in the human body using artificial intelligence and machine learning - p. 106 14. Chapter 7 Identification and classification of rheumatoid arthritis using artificial intelligence and machine learning - p. 124 15. Chapter 8 Early detection approach for analysis of osteoarthritis using artificial intelligence and machine learning - p. 148 16. Chapter 9 Preanalysis of ankylosing spondylitis using machine learning - p. 168 17. Chapter 10 Analysis and identification of gout flares using machine learning - p. 180 18. Chapter 11 Impact of amyloidosis on bones and its relationship to dementia - p. 196 19. Chapter 12 Machine learning for bone deformation detection in real-world applications - p. 224 20. Chapter 13 Artificial intelligence and machine learning for foot and ankle disorders - p. 244 21. Chapter 14 Conclusion: A future perspective on diagnosing musculoskeletal conditions using artificial intelligence and machine learning - p. 268 22. Index - p. 285
