Buch, Englisch, 208 Seiten
Reihe: ISTE Invoiced
Advances in Intelligent Imaging, Decision Support, and Rehabilitation
Buch, Englisch, 208 Seiten
Reihe: ISTE Invoiced
ISBN: 978-1-83669-138-9
Verlag: ISTE Ltd
AI in Orthopedic Trauma Care 2 focuses on predictive modeling and data-driven strategies across the continuum of trauma care. Framed within precision healthcare, it examines how AI-driven systems enhance decision-making, risk assessment and recovery planning.
This book integrates machine learning and clinical data analytics to predict fracture union, assess osteoporosis and implant failure risk, evaluate infection probability, and support polytrauma management. It further explores functional recovery modeling and personalized rehabilitation pathways.
Emphasizing responsible AI integration, this book addresses ethical governance, data security, transparency and implementation challenges, offering practical guidance for clinicians, healthcare technologists and researchers building intelligent, secure, and patient-centered orthopedic trauma systems.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface xiii
Abhishek KUMAR and Priya BATTA
Introduction xv
Abhishek KUMAR and Priya BATTA
Chapter 1. Prediction of Fracture Union in Long Bones Using AI Models 1
Santosh TAKALE and Dalavi Surekha GORAKH
1.1. Introduction 1
1.2. Types of AI models for fracture union prediction 3
1.3. Data sources and predictive variables 5
1.4. Integration of imaging with AI 6
1.5. Performance of AI models in predicting fracture union 7
1.6. Challenges and limitations 9
1.7. Future directions 10
1.8. Conclusion 11
1.9. References 11
Chapter 2. Osteoporosis Risk Assessment in Fragility Fractures Supported by AI Analysis 15
Sandip R. PATIL and Chavan Vikrant U.
2.1. Introduction 15
2.2. Fragility fractures and osteoporosis: clinical significance 17
2.3. Conventional osteoporosis risk assessment methods 19
2.4. AI in medical imaging for osteoporosis 21
2.5. ML models for fracture risk prediction 22
2.6. AI-supported clinical decision systems in fragility fractures 23
2.7. Challenges, ethical considerations and data limitations 24
2.8. Conclusion 26
2.9. References 27
Chapter 3. Postoperative Alignment Assessment After Total Knee Arthroplasty with AI Assistance 31
Channapa MAHAJAN and More Seema NITIN
3.1. Introduction 31
3.2. Conventional methods for postoperative alignment assessment after total knee arthroplasty 33
3.3. AI in orthopedic imaging: fundamental concepts 35
3.4. AI-assisted postoperative alignment assessment techniques after total knee arthroplasty 38
3.5. Clinical validation and comparative effectiveness of AI-assisted alignment assessment 42
3.6. Challenges, limitations and ethical considerations 43
3.7. Future directions and emerging trends 45
3.8. Conclusion 45
3.9. References 46
Chapter 4. Functional Outcome Prediction Following Open Tibial Fractures Using AI-Based Models 51
Amol S. DHAWALE and Dalavi Surekha GORAKH
4.1. Introduction 51
4.2. Clinical significance of open tibial fractures 52
4.3. Traditional approaches to outcome prediction 54
4.4. Overview of artificial intelligence in orthopedic trauma 55
4.5. ML techniques for functional outcome prediction 56
4.6. Data sources and feature engineering 57
4.7. Patient-reported outcome measures in AI-based prediction models 58
4.8. Prediction of rehabilitation trajectories and return to work 59
4.9. Comparison of AI-based models with traditional predictive methods 60
4.10. Clinical applications of AI-based functional outcome prediction 61
4.11. Challenges and limitations of AI-based models 62
4.12. Ethical, legal and regulatory considerations 63
4.13. Conclusion 64
4.14. References 65
Chapter 5. Implant Failure Risk Stratification in Orthopedic Trauma Through AI 69
Paresh PATIL and Dalavi Surekha GORAKH
5.1. Introduction 69
5.2. Implant failure in orthopedic trauma: definitions and clinical significance 72
5.3. Rationale for AI in implant failure risk stratification 74
5.4. Data sources for AI-based implant failure prediction 74
5.5. ML and DL techniques 77
5.6. Performance metrics and model evaluation 79
5.7. Clinical applications of AI-based risk stratification 80
5.8. Ethical, regulatory and implementation challenges 81
5.9. Conclusion 82
5.10. References 82
Chapter 6. Surgical Site Infection Risk Assessment in Orthopedic Procedures Using AI Tools 87
Jineshwar KAPALE and Attar Tabassum M.
6.1. Overview of surgical site infections in orthopedic procedures 87
6.2. Risk factors for SSIs in orthopedic surgery 89
6.3. Limitations of conventional SSI risk assessment models 91
6.4. AI and ML in healthcare 93
6.5. AI-based models for SSI risk assessment in orthopedic procedures 95
6.6. Data sources and feature engineering for AI-driven SSI prediction 96
6.7. Clinical integration, ethical issues and implementation issues 97
6.8. Conclusion 99
6.9. References 99
Chapter 7. Functional Recovery Analysis After Hip Fracture Surgery Supported by AI Analytics 103
P.N. KULKARNI and Pisal Rutuja A.
7.1. Introduction 104
7.2. Functional recovery after hip fracture: concepts and measurement 105
7.3. AI and ML in clinical outcome prediction 108
7.4. Digital health, wearable analytics and AI-supported monitoring of recovery 112
7.5. Implementation challenges in AI-supported functional recovery analysis 113
7.6. AI-enabled personalization of rehabilitation and care pathways 114
7.7. Patient-centered perspectives and ethical considerations 115
7.8. Future directions in AI-supported functional recovery analysis 116
7.9. Conclusion 117
7.10. References 117
Chapter 8. Radiographic Evaluation of Bone Healing After Intramedullary Nailing with AI Support 121
Anupam S. KOLEKAR and More Seema NITIN
8.1. Introduction 121
8.2. Biological and mechanical principles of bone healing after intramedullary nailing 124
8.3. Conventional radiographic assessment of bone healing 125
8.4. Limitations of traditional radiographic evaluation 126
8.5. AI in orthopedic imaging 128
8.6. AI-based radiographic evaluation of bone healing 129
8.7. AI models and algorithms used in bone healing assessment 131
8.8. Integration of AI into clinical workflows 132x AI in Orthopedic Trauma Care 2
8.9. Clinical impact and outcome improvement 133
8.10. Challenges, limitations and ethical considerations 134
8.11. Future directions and research opportunities 135
8.12. Conclusion 136
8.13. References 136
Chapter 9. Decision-Making in Polytrauma Management Enhanced by AI 139
Vikas DESHMUKH and Piyush Ashokrao DALKE
9.1. Introduction 139
9.2. Decision-making challenges in polytrauma management 141
9.3. AI technologies in polytrauma care 142
9.4. AI in prehospital polytrauma management 143
9.5. Emergency department triage and early assessment 144
9.6. AI-enhanced imaging and diagnostic decision-making 145
9.7. AI in surgical decision-making and operative planning 146
9.8. AI in intensive care management of polytrauma 147
9.9. Outcome prediction and prognostication using AI 148
9.10. Ethical, legal and implementation challenges of AI in polytrauma care 150
9.11. Future directions and emerging trends in AI-driven polytrauma decision-making 151
9.12. Conclusion 152
9.13. References 152
Chapter 10. Outcome Prediction After Rotator Cuff Repair Using AI-Assisted Clinical Models. 157
Vikas DESHMUKH and Prashant S. JADHAV
10.1. Introduction 157
10.2. Clinical outcomes after rotator cuff repair 160
10.3. Limitations of conventional predictive models 161
10.4. AI and ML in orthopedics 161
10.5. Data sources for AI-assisted outcome prediction 162
10.6. AI models for predicting tendon healing and re-tear risk 163
10.7. Prediction of functional recovery and pain outcomes 164
10.8. Integration of wearable and rehabilitation data 164
10.9. Model performance and validation 165
10.10. Interpretability and clinical acceptance 167
10.11. Ethical, legal and practical considerations 167
10.12. Future directions 168Contents xi
10.13. Implications for precision medicine 169
10.14. Conclusion 169
10.15. References 170
List of Authors 173
Index 177
