AI in Orthopedic Trauma Care 2

Abhishek Kumar is a senior IEEE member and professor at Chandigarh University in Mohali, India. His research expertise spans AI, renewable energy and image processing.Priya Batta is an associate professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. Her research specializes in AI, blockchain and the IoT.

• Preface xiiiAbhishek KUMAR and Priya BATTAIntroduction xvAbhishek KUMAR and Priya BATTAChapter 1. Prediction of Fracture Union in Long Bones Using AI Models 1Santosh TAKALE and Dalavi Surekha GORAKH1.1. Introduction 11.2. Types of AI models for fracture union prediction 31.3. Data sources and predictive variables 51.4. Integration of imaging with AI 61.5. Performance of AI models in predicting fracture union 71.6. Challenges and limitations 91.7. Future directions 101.8. Conclusion 111.9. References 11Chapter 2. Osteoporosis Risk Assessment in Fragility Fractures Supported by AI Analysis 15Sandip R. PATIL and Chavan Vikrant U.2.1. Introduction 152.2. Fragility fractures and osteoporosis: clinical significance 172.3. Conventional osteoporosis risk assessment methods 192.4. AI in medical imaging for osteoporosis 212.5. ML models for fracture risk prediction 222.6. AI-supported clinical decision systems in fragility fractures 232.7. Challenges, ethical considerations and data limitations 242.8. Conclusion 262.9. References 27Chapter 3. Postoperative Alignment Assessment After Total Knee Arthroplasty with AI Assistance 31Channapa MAHAJAN and More Seema NITIN3.1. Introduction 313.2. Conventional methods for postoperative alignment assessment after total knee arthroplasty 333.3. AI in orthopedic imaging: fundamental concepts 353.4. AI-assisted postoperative alignment assessment techniques after total knee arthroplasty 383.5. Clinical validation and comparative effectiveness of AI-assisted alignment assessment 423.6. Challenges, limitations and ethical considerations 433.7. Future directions and emerging trends 453.8. Conclusion 453.9. References 46Chapter 4. Functional Outcome Prediction Following Open Tibial Fractures Using AI-Based Models 51Amol S. DHAWALE and Dalavi Surekha GORAKH4.1. Introduction 514.2. Clinical significance of open tibial fractures 524.3. Traditional approaches to outcome prediction 544.4. Overview of artificial intelligence in orthopedic trauma 554.5. ML techniques for functional outcome prediction 564.6. Data sources and feature engineering 574.7. Patient-reported outcome measures in AI-based prediction models 584.8. Prediction of rehabilitation trajectories and return to work 594.9. Comparison of AI-based models with traditional predictive methods 604.10. Clinical applications of AI-based functional outcome prediction 614.11. Challenges and limitations of AI-based models 624.12. Ethical, legal and regulatory considerations 634.13. Conclusion 644.14. References 65Chapter 5. Implant Failure Risk Stratification in Orthopedic Trauma Through AI 69Paresh PATIL and Dalavi Surekha GORAKH5.1. Introduction 695.2. Implant failure in orthopedic trauma: definitions and clinical significance 725.3. Rationale for AI in implant failure risk stratification 745.4. Data sources for AI-based implant failure prediction 745.5. ML and DL techniques 775.6. Performance metrics and model evaluation 795.7. Clinical applications of AI-based risk stratification 805.8. Ethical, regulatory and implementation challenges 815.9. Conclusion 825.10. References 82Chapter 6. Surgical Site Infection Risk Assessment in Orthopedic Procedures Using AI Tools 87Jineshwar KAPALE and Attar Tabassum M.6.1. Overview of surgical site infections in orthopedic procedures 876.2. Risk factors for SSIs in orthopedic surgery 896.3. Limitations of conventional SSI risk assessment models 916.4. AI and ML in healthcare 936.5. AI-based models for SSI risk assessment in orthopedic procedures 956.6. Data sources and feature engineering for AI-driven SSI prediction 966.7. Clinical integration, ethical issues and implementation issues 976.8. Conclusion 996.9. References 99Chapter 7. Functional Recovery Analysis After Hip Fracture Surgery Supported by AI Analytics 103P.N. KULKARNI and Pisal Rutuja A.7.1. Introduction 1047.2. Functional recovery after hip fracture: concepts and measurement 1057.3. AI and ML in clinical outcome prediction 1087.4. Digital health, wearable analytics and AI-supported monitoring of recovery 1127.5. Implementation challenges in AI-supported functional recovery analysis 1137.6. AI-enabled personalization of rehabilitation and care pathways 1147.7. Patient-centered perspectives and ethical considerations 1157.8. Future directions in AI-supported functional recovery analysis 1167.9. Conclusion 1177.10. References 117Chapter 8. Radiographic Evaluation of Bone Healing After Intramedullary Nailing with AI Support 121Anupam S. KOLEKAR and More Seema NITIN8.1. Introduction 1218.2. Biological and mechanical principles of bone healing after intramedullary nailing 1248.3. Conventional radiographic assessment of bone healing 1258.4. Limitations of traditional radiographic evaluation 1268.5. AI in orthopedic imaging 1288.6. AI-based radiographic evaluation of bone healing 1298.7. AI models and algorithms used in bone healing assessment 1318.8. Integration of AI into clinical workflows 132x AI in Orthopedic Trauma Care 28.9. Clinical impact and outcome improvement 1338.10. Challenges, limitations and ethical considerations 1348.11. Future directions and research opportunities 1358.12. Conclusion 1368.13. References 136Chapter 9. Decision-Making in Polytrauma Management Enhanced by AI 139Vikas DESHMUKH and Piyush Ashokrao DALKE9.1. Introduction 1399.2. Decision-making challenges in polytrauma management 1419.3. AI technologies in polytrauma care 1429.4. AI in prehospital polytrauma management 1439.5. Emergency department triage and early assessment 1449.6. AI-enhanced imaging and diagnostic decision-making 1459.7. AI in surgical decision-making and operative planning 1469.8. AI in intensive care management of polytrauma 1479.9. Outcome prediction and prognostication using AI 1489.10. Ethical, legal and implementation challenges of AI in polytrauma care 1509.11. Future directions and emerging trends in AI-driven polytrauma decision-making 1519.12. Conclusion 1529.13. References 152Chapter 10. Outcome Prediction After Rotator Cuff Repair Using AI-Assisted Clinical Models. 157Vikas DESHMUKH and Prashant S. JADHAV10.1. Introduction 15710.2. Clinical outcomes after rotator cuff repair 16010.3. Limitations of conventional predictive models 16110.4. AI and ML in orthopedics 16110.5. Data sources for AI-assisted outcome prediction 16210.6. AI models for predicting tendon healing and re-tear risk 16310.7. Prediction of functional recovery and pain outcomes 16410.8. Integration of wearable and rehabilitation data 16410.9. Model performance and validation 16510.10. Interpretability and clinical acceptance 16710.11. Ethical, legal and practical considerations 16710.12. Future directions 168Contents xi10.13. Implications for precision medicine 16910.14. Conclusion 16910.15. References 170List of Authors 173Index 177