Artificial Intelligence for Bone Disorder

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. He has edited multiple volumes for Wiley-Scrivener. Shivam Rajput completed his MPharm. at Galgotias University, Greater Noida, India. He is currently an assistant professor at IITM College of Pharmacy, Sonipat, Hariyana, India. His areas of research include nanoformulations, cancer nanomedicine, and green nanotechnology for therapeutic applications. Makarand Vaidya completed his Master of Surgery in Orthopaedics from Hindu Rao Hospital, Delhi where he is currently a faculty member. He has had a distinguished academic career and has published many research papers and books.

• Foreword xiPreface xiii1 Artificial Intelligence and Bone Fracture Detection: An Unexpected Alliance 11.1 Introduction 11.2 Bone Fracture 31.3 Deep Learning and Its Significance in Radiology 41.4 Role of AI in Bone Fracture Detection and Its Application 61.5 Primary Machine Learning-Based Algorithm in Bone Fracture Detection 91.6 Deep Learning-Based Techniques for Fracture Detection 131.7 Conclusion 182 Integrating AI With Tissue Engineering: The Next Step in Bone Regeneration 252.1 Introduction 252.2 Anatomy and Biology of Bone 262.3 Bone Regeneration Mechanism 282.4 Understanding AI 352.5 Current AI Integration 392.6 Applying Deep Learning 422.7 Conclusion 463 Deep Supervised Learning on Radiological Images to Classify Bone Fractures: A Novel Approach 593.1 Introduction 593.2 Common Bone Disorder 623.3 Deep Supervised Learning's Importance in Orthopedics and Radiology 633.4 Perspective From the Past 633.5 Essential Deep Learning Methods for Bone Imaging 653.6 Strategies for Effective Annotation 693.7 Application of Deep Learning to the Detection of Fractures 693.8 Conclusion 734 Treatment of Osteoporosis and the Use of Digital Health Intervention 794.1 Introduction 794.2 Opportunistic Diagnosis of Osteoporosis 824.3 Predictive Models 854.4 Assessment of Fracture Risk and Osteoporosis Diagnosis by Digital Health 904.5 Clinical Decision Support Tools, Reminders, and Prompts for Spotting Osteoporosis in Digital Health Settings 914.6 The Role of Digital Health in Facilitating Patient Education, Decision, and Conversation 934.7 Conclusion 955 Utilizing AI to Improve Orthopedic Care 1055.1 Introduction 1055.2 What is AI? 1065.3 Introduction to Machine Learning: Algorithms and Applications 1095.4 Natural Language Processing 1145.5 The Internet of Things 1155.6 Prospective AI Advantages in Orthopedics 1165.7 Diagnostic Application of AI 1185.8 Prediction Application With AI 1215.9 Conclusion 1246 Significance of Artificial Intelligence in Spinal Disorder Treatment 1336.1 Introduction 1336.2 Machine Learning 1356.3 Methods Derived From Statistics 1376.4 Applications of Machine Learning in Spine Surgery 1436.5 Application of AI and ML in Spine Research 1476.6 Conclusion 1567 Osteoporosis Biomarker Identification and Use of Machine Learning in Osteoporosis Treatment 1697.1 Introduction 1697.2 Biomarkers of Bone Development 1727.3 Biomarkers for Bone Resorption 1757.4 Regulators of Bone Turnover 1797.5 Methods to Identify Osteoporosis 1807.6 Conclusion 1878 The Role of AI in Pediatric Orthopedics 1978.1 Introduction 1978.2 Strategy Based on Artificial Intelligence 2008.3 Several Applications of Artificial Intelligence 2078.4 Conclusion 2129 Use of Artificial Intelligence in Imaging for Bone Cancer 2199.1 Introduction 2199.2 Applications of Machine Learning to Cancer Diagnosis 2239.3 Artificial Intelligence Methods for Diagnosing Bone Cancer 2269.4 Methodologies for Constructing Deep Learning Model 2279.5 Clinical Image Applications of Deep Learning for Bone Tumors 2299.6 Conclusion 236References 236Index 245