{"product_id":"ai-in-orthopedic-trauma-care-2-advances-in-intelligent-imaging-decision-support-and-rehabilitation-hardback-9781836691389","title":"AI in Orthopedic Trauma Care 2; Advances in Intelligent Imaging, Decision Support, and Rehabilitation (Hardback) 9781836691389","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI in Orthopedic Trauma Care 2\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAdvances in Intelligent Imaging, Decision Support, and Rehabilitation\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbhishek Kumar (Edited by), Kumar (Author), Priya Batta (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781836691389, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 July 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e208 pages\u003cbr\u003e23.5 x 15.6 x 1.5 cm, 0.422 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eAI in Orthopedic Trauma Care 2\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eEmphasizing 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003cbr\u003e\u003ci\u003eAbhishek KUMAR and Priya BATTA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003cbr\u003e\u003ci\u003eAbhishek KUMAR and Priya BATTA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Prediction of Fracture Union in Long Bones Using AI Models 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSantosh TAKALE and Dalavi Surekha GORAKH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003cbr\u003e1.2. Types of AI models for fracture union prediction 3\u003cbr\u003e1.3. Data sources and predictive variables 5\u003cbr\u003e1.4. Integration of imaging with AI 6\u003cbr\u003e1.5. Performance of AI models in predicting fracture union 7\u003cbr\u003e1.6. Challenges and limitations 9\u003cbr\u003e1.7. Future directions 10\u003cbr\u003e1.8. Conclusion 11\u003cbr\u003e1.9. References 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Osteoporosis Risk Assessment in Fragility Fractures Supported by AI Analysis 15\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSandip R. PATIL and Chavan Vikrant U.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 15\u003cbr\u003e2.2. Fragility fractures and osteoporosis: clinical significance 17\u003cbr\u003e2.3. Conventional osteoporosis risk assessment methods 19\u003cbr\u003e2.4. AI in medical imaging for osteoporosis 21\u003cbr\u003e2.5. ML models for fracture risk prediction 22\u003cbr\u003e2.6. AI-supported clinical decision systems in fragility fractures 23\u003cbr\u003e2.7. Challenges, ethical considerations and data limitations 24\u003cbr\u003e2.8. Conclusion 26\u003cbr\u003e2.9. References 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Postoperative Alignment Assessment After Total Knee Arthroplasty with AI Assistance 31\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChannapa MAHAJAN and More Seema NITIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 31\u003cbr\u003e3.2. Conventional methods for postoperative alignment assessment after total knee arthroplasty 33\u003cbr\u003e3.3. AI in orthopedic imaging: fundamental concepts 35\u003cbr\u003e3.4. AI-assisted postoperative alignment assessment techniques after total knee arthroplasty 38\u003cbr\u003e3.5. Clinical validation and comparative effectiveness of AI-assisted alignment assessment 42\u003cbr\u003e3.6. Challenges, limitations and ethical considerations 43\u003cbr\u003e3.7. Future directions and emerging trends 45\u003cbr\u003e3.8. Conclusion 45\u003cbr\u003e3.9. References 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Functional Outcome Prediction Following Open Tibial Fractures Using AI-Based Models 51\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAmol S. DHAWALE and Dalavi Surekha GORAKH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 51\u003cbr\u003e4.2. Clinical significance of open tibial fractures 52\u003cbr\u003e4.3. Traditional approaches to outcome prediction 54\u003cbr\u003e4.4. Overview of artificial intelligence in orthopedic trauma 55\u003cbr\u003e4.5. ML techniques for functional outcome prediction 56\u003cbr\u003e4.6. Data sources and feature engineering 57\u003cbr\u003e4.7. Patient-reported outcome measures in AI-based prediction models 58\u003cbr\u003e4.8. Prediction of rehabilitation trajectories and return to work 59\u003cbr\u003e4.9. Comparison of AI-based models with traditional predictive methods 60\u003cbr\u003e4.10. Clinical applications of AI-based functional outcome prediction 61\u003cbr\u003e4.11. Challenges and limitations of AI-based models 62\u003cbr\u003e4.12. Ethical, legal and regulatory considerations 63\u003cbr\u003e4.13. Conclusion 64\u003cbr\u003e4.14. References 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Implant Failure Risk Stratification in Orthopedic Trauma Through AI 69\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eParesh PATIL and Dalavi Surekha GORAKH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 69\u003cbr\u003e5.2. Implant failure in orthopedic trauma: definitions and clinical significance 72\u003cbr\u003e5.3. Rationale for AI in implant failure risk stratification 74\u003cbr\u003e5.4. Data sources for AI-based implant failure prediction 74\u003cbr\u003e5.5. ML and DL techniques 77\u003cbr\u003e5.6. Performance metrics and model evaluation 79\u003cbr\u003e5.7. Clinical applications of AI-based risk stratification 80\u003cbr\u003e5.8. Ethical, regulatory and implementation challenges 81\u003cbr\u003e5.9. Conclusion 82\u003cbr\u003e5.10. References 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Surgical Site Infection Risk Assessment in Orthopedic Procedures Using AI Tools 87\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJineshwar KAPALE and Attar Tabassum M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Overview of surgical site infections in orthopedic procedures 87\u003cbr\u003e6.2. Risk factors for SSIs in orthopedic surgery 89\u003cbr\u003e6.3. Limitations of conventional SSI risk assessment models 91\u003cbr\u003e6.4. AI and ML in healthcare 93\u003cbr\u003e6.5. AI-based models for SSI risk assessment in orthopedic procedures 95\u003cbr\u003e6.6. Data sources and feature engineering for AI-driven SSI prediction 96\u003cbr\u003e6.7. Clinical integration, ethical issues and implementation issues 97\u003cbr\u003e6.8. Conclusion 99\u003cbr\u003e6.9. References 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Functional Recovery Analysis After Hip Fracture Surgery Supported by AI Analytics 103\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP.N. KULKARNI and Pisal Rutuja A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 104\u003cbr\u003e7.2. Functional recovery after hip fracture: concepts and measurement 105\u003cbr\u003e7.3. AI and ML in clinical outcome prediction 108\u003cbr\u003e7.4. Digital health, wearable analytics and AI-supported monitoring of recovery 112\u003cbr\u003e7.5. Implementation challenges in AI-supported functional recovery analysis 113\u003cbr\u003e7.6. AI-enabled personalization of rehabilitation and care pathways 114\u003cbr\u003e7.7. Patient-centered perspectives and ethical considerations 115\u003cbr\u003e7.8. Future directions in AI-supported functional recovery analysis 116\u003cbr\u003e7.9. Conclusion 117\u003cbr\u003e7.10. References 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Radiographic Evaluation of Bone Healing After Intramedullary Nailing with AI Support 121\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAnupam S. KOLEKAR and More Seema NITIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 121\u003cbr\u003e8.2. Biological and mechanical principles of bone healing after intramedullary nailing 124\u003cbr\u003e8.3. Conventional radiographic assessment of bone healing 125\u003cbr\u003e8.4. Limitations of traditional radiographic evaluation 126\u003cbr\u003e8.5. AI in orthopedic imaging 128\u003cbr\u003e8.6. AI-based radiographic evaluation of bone healing 129\u003cbr\u003e8.7. AI models and algorithms used in bone healing assessment 131\u003cbr\u003e8.8. Integration of AI into clinical workflows 132x AI in Orthopedic Trauma Care 2\u003cbr\u003e8.9. Clinical impact and outcome improvement 133\u003cbr\u003e8.10. Challenges, limitations and ethical considerations 134\u003cbr\u003e8.11. Future directions and research opportunities 135\u003cbr\u003e8.12. Conclusion 136\u003cbr\u003e8.13. References 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Decision-Making in Polytrauma Management Enhanced by AI 139\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVikas DESHMUKH and Piyush Ashokrao DALKE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 139\u003cbr\u003e9.2. Decision-making challenges in polytrauma management 141\u003cbr\u003e9.3. AI technologies in polytrauma care 142\u003cbr\u003e9.4. AI in prehospital polytrauma management 143\u003cbr\u003e9.5. Emergency department triage and early assessment 144\u003cbr\u003e9.6. AI-enhanced imaging and diagnostic decision-making 145\u003cbr\u003e9.7. AI in surgical decision-making and operative planning 146\u003cbr\u003e9.8. AI in intensive care management of polytrauma 147\u003cbr\u003e9.9. Outcome prediction and prognostication using AI 148\u003cbr\u003e9.10. Ethical, legal and implementation challenges of AI in polytrauma care 150\u003cbr\u003e9.11. Future directions and emerging trends in AI-driven polytrauma decision-making 151\u003cbr\u003e9.12. Conclusion 152\u003cbr\u003e9.13. References 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Outcome Prediction After Rotator Cuff Repair Using AI-Assisted Clinical Models. 157\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVikas DESHMUKH and Prashant S. JADHAV\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 157\u003cbr\u003e10.2. Clinical outcomes after rotator cuff repair 160\u003cbr\u003e10.3. Limitations of conventional predictive models 161\u003cbr\u003e10.4. AI and ML in orthopedics 161\u003cbr\u003e10.5. Data sources for AI-assisted outcome prediction 162\u003cbr\u003e10.6. AI models for predicting tendon healing and re-tear risk 163\u003cbr\u003e10.7. Prediction of functional recovery and pain outcomes 164\u003cbr\u003e10.8. Integration of wearable and rehabilitation data 164\u003cbr\u003e10.9. Model performance and validation 165\u003cbr\u003e10.10. Interpretability and clinical acceptance 167\u003cbr\u003e10.11. Ethical, legal and practical considerations 167\u003cbr\u003e10.12. Future directions 168Contents xi\u003cbr\u003e10.13. Implications for precision medicine 169\u003cbr\u003e10.14. Conclusion 169\u003cbr\u003e10.15. References 170\u003c\/p\u003e \u003cp\u003eList of Authors 173\u003cbr\u003eIndex 177\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Clinical \u0026amp; internal medicine [\u003ca title=\"See our other books on Clinical \u0026amp; internal medicine\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Clinical%20\u0026amp;%20internal%20medicine%20%5BMJ%5D%22\"\u003eMJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446833017112,"sku":"9781836691389","price":135.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781836691389.jpg?v=1785115195","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/ai-in-orthopedic-trauma-care-2-advances-in-intelligent-imaging-decision-support-and-rehabilitation-hardback-9781836691389","provider":"Freshly Printed Books","version":"1.0","type":"link"}