AI-Driven Innovations in Physiotherapy and Oncology 3

• Autore: Kumar Abhishek
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2026; ©2026
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (425 pages)
• Disciplina: 616.994062028563
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy -- 1.1. Introduction -- 1.2. Fundamentals of reinforcement learning (RL) -- 1.2.1. Overview -- 1.2.2. Key components in cancer rehabilitation -- 1.3. Cancer rehabilitation needs and challenges -- 1.3.1. Functional deficits post-cancer treatment -- 1.3.2. Challenges in current rehabilitation protocols -- 1.4. RL-based framework for adaptive rehabilitation -- 1.4.1. System architecture -- 1.4.2. Data sources -- 1.4.3. Adaptive learning process -- 1.5. Benefits of RL in cancer physiotherapy -- 1.6. Limitations and ethical considerations -- 1.6.1. Privacy and security issues -- 1.6.2. Data bias -- 1.6.3. Interpretability -- 1.6.4. Dependency and dehumanization -- 1.7. Future directions -- 1.7.1. Multi-agent reinforcement learning (RL) -- 1.7.2. Transfer learning -- 1.7.3. Human-in-the-loop approaches -- 1.7.4. Integration with electronic health records (EHRs) -- 1.8. Conclusion -- 1.9. References -- Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation -- 2.1. Introduction -- 2.2. Clinical background: gait and balance in cancer survivors -- 2.3. AI-enabled gait and balance assessment: overview of technologies -- 2.3.1. Markerless computer vision/pose estimation -- 2.3.2. Wearable IMUs and smart sensing systems -- 2.3.3. Telehealth and mobile-based systems -- 2.3.4. Learnable and privacy-protective AI -- 2.3.5. VR and rehabilitation robotics are transforming -- 2.4. Evidence base and validation in the oncology setting -- 2.5. Core components of an AI-enabled oncology gaitâ€"balance platform -- 2.5.1. Non-intrusive and accessible data capture -- 2.5.2. AI models and analytics -- 2.5.3. Dashboard and reporting interface -- 2.5.4. Integration with rehabilitation prescription. ; 2.6. Implementation in oncology settings: use cases and workflow -- 2.6.1. Baseline and ongoing assessment -- 2.6.2. Personalized intervention and progress monitoring -- 2.6.3. Fall risk stratification and safety monitoring -- 2.6.4. Remote telerehabilitation -- 2.7. Challenges and considerations -- 2.7.1. Data quality and clinical validation -- 2.7.2. Bias and generalizability -- 2.7.3. Regulatory and integration hurdles -- 2.7.4. Patient engagement and equity. -- 2.8. Future directions and research needs -- 2.9. Conclusion -- 2.10. References -- Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs -- 3.1. Introduction -- 3.2. Cancer-related fatigue and physiotherapy -- 3.3. Traditional and sensor-based monitoring -- 3.4. Deep learning models for fatigue monitoring -- 3.4.1. Forecasting symptom escalation -- 3.4.2. Sensor-based and multimodal fatigue -- 3.4.3. Assessing rehabilitation exercise -- 3.5. Integration into cancer physiotherapy -- 3.6. Applications and use cases -- 3.6.1. Real-time fatigue monitoring -- 3.6.2. Predictive warnings -- 3.6.3. Personalized therapy -- 3.7. Strengths, limitations and challenges -- 3.7.1. Strengths of DL-driven fatigue monitoring systems -- 3.7.2. Limitations and gaps in the current research -- 3.7.3. Ethical and operational challenges -- 3.8. Future directions -- 3.9. Conclusion -- 3.10. References -- Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy -- 4.1. Introduction -- 4.2. Clinical background: lymphedema in oncology -- 4.3. Rationale for predictive modeling -- 4.4. AI and ML overview -- 4.5. Model development approaches -- 4.5.1. Data collection -- 4.5.2. Feature engineering and selection -- 4.5.3. Model training and validation -- 4.6. Performance metrics and model comparisons -- 4.7. Explainability and clinical integration. ; 4.8. Role in oncology physiotherapy -- 4.9. Challenges and limitations -- 4.10. Future directions -- 4.11. Conclusion -- 4.12. References -- Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation -- 5.1. Introduction -- 5.2. Impact of chemotherapy on physical function -- 5.2.1. Common side effects affecting mobility -- 5.2.2. Limitations of traditional movement assessment -- 5.2.3. Need for advanced assessment tools -- 5.3. AI technologies for movement quality assessment -- 5.3.1. Motion capture systems -- 5.3.2. Pose estimation and computer vision -- 5.3.3. Machine learning and deep learning models -- 5.3.4. Scoring metrics and interpretability -- 5.3.5. Real-time feedback systems -- 5.4. Clinical applications in post-chemotherapy rehabilitation -- 5.4.1. Gait analysis -- 5.4.2. Upper limb function assessment -- 5.4.3. Balance and fall risk detection -- 5.4.4. Remote rehabilitation and telehealth -- 5.5. Challenges and limitations -- 5.5.1. Data limitations -- 5.5.2. Interpatient variability -- 5.5.3. Sensor and environment constraints -- 5.5.4. Privacy and ethical issues -- 5.5.5. Clinical integration -- 5.6. Future directions and opportunities -- 5.6.1. Individualized rehabilitation programs -- 5.6.2. Multi-modal data fusion -- 5.6.3. Federated learning and data sharing -- 5.6.4. Gamification and engagement -- 5.6.5. Cross-domain application -- 5.7. Conclusion -- 5.8. References -- Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy -- 6.1. Introduction -- 6.2. Cancer pain: scope and challenges -- 6.2.1. Prevalence and impact -- 6.2.2. Traditional physiotherapy weakness -- 6.3. VR in pain management -- 6.3.1. What VR? -- 6.3.2. Mechanisms of VR for pain relief -- 6.3.3. Clinical applications of VR in cancer physiotherapy -- 6.3.4. Evidence-based observations -- 6.4. AI in pain management. ; 6.4.1. Role of AI in healthcare -- 6.4.2. AI in physiotherapy -- 6.4.3. AI-driven personalization -- 6.5. Integrating VR and AI: a synergistic approach -- 6.5.1. Real-time adaptive VR systems -- 6.5.2. Closed-loop feedback systems -- 6.5.3. Gamification and behavioral reinforcement -- 6.6. Case studies and clinical implementations -- 6.6.1. Use case I -- 6.6.2. Use case II -- 6.6.3. Pediatric cancer and VR distraction therapy -- 6.7. Technical and ethical considerations -- 6.7.1. Data privacy and consent -- 6.7.2. Bias in AI algorithms -- 6.7.3. Technology access and digital divide -- 6.8. Future directions -- 6.8.1. Multimodal integration -- 6.8.2. Remote and home-based rehabilitation -- 6.8.3. Prediction of chronic pain -- 6.8.4. AI sources and virtual therapists -- 6.9. Conclusion -- 6.10. References -- Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation -- 7.1. Introduction -- 7.2. Exercise intensity in oncology rehabilitation -- 7.2.1. Defining exercise intensity -- 7.2.2. Challenges in modulating intensity -- 7.3. Machine learning in healthcare and rehabilitation -- 7.3.1. Machine learning overview -- 7.3.2. Machine learning (ML) in broader rehabilitation contexts -- 7.4. ML techniques for exercise intensity optimization -- 7.4.1. Supervised learning for exercise intensity prediction -- 7.4.2. Unsupervised learning and clustering for patient stratification -- 7.4.3. Reinforcement learning (RL) for adaptive exercise prescription -- 7.5. Data sources for ML modeling -- 7.5.1. Wearable devices and biosensors -- 7.5.2. Patient-reported outcomes (PROs) -- 7.5.3. Electronic health records (EHRs) -- 7.6. Challenges and limitations -- 7.6.1. Data quality and heterogeneity -- 7.6.2. Model interpretability. -- 7.6.3. Ethical and legal issues -- 7.6.4. Clinical validation -- 7.7. Future directions. ; 7.7.1. Multimodal data fusion -- 7.7.2. Customizable digital twin -- 7.7.3. Federated learning -- 7.7.4. Integration with genomic data -- 7.8. Conclusion -- 7.9. References -- Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care -- 8.1. Introduction -- 8.2. Background and theoretical framework -- 8.2.1. Digital twins in healthcare -- 8.2.2. Musculoskeletal and physiotherapy digital twins -- 8.2.3. Virtual physiological human (VPH) and multiscale modeling -- 8.2.4. The role of artificial intelligence -- 8.3. Current research landscape -- 8.3.1. Bibliometric and thematic mapping -- 8.3.2. Oncology-focused DT research -- 8.3.3. Technology prototypes in rehabilitation -- 8.4. Framework for AI-driven DT in cancer physiotherapy -- 8.4.1. Data modalities and integration -- 8.4.2. Modeling and simulation architecture -- 8.4.3. Simulation of physiotherapy interventions -- 8.5. Use cases and scenario examples -- 8.5.1. Post-mastectomy shoulder rehabilitation -- 8.5.2. Cancer-related fatigue and gait training -- 8.5.3. Movement optimization and pain management -- 8.5.4. Telerehabilitation scaling -- 8.6. Evidence of effectiveness -- 8.6.1. Outcome results -- 8.6.2. Oncology DT impact -- 8.6.3. Analogous systems in MSK physiotherapy -- 8.7. Ethical, practical and regulatory challenges -- 8.7.1. Data privacy and sharing -- 8.7.2. Model explainability and clinician trust -- 8.7.3. Equity and accessibility -- 8.7.4. The computational and resource challenges represent -- 8.7.5. Legal liability -- 8.8. Future directions and research agenda -- 8.8.1. Clinical trials -- 8.8.2. Multi-mode data integration -- 8.8.3. Advanced AI methods -- 8.8.4. AR/VR and gamification -- 8.8.5. Regulatory frameworks -- 8.8.6. Interdisciplinary teamwork -- 8.9. Conclusion -- 8.10. References. ; Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy.
• Sommario/riassunto: AI-driven Innovations in Physiotherapy and Oncology 3 is positioned at the intersection of artificial intelligence (AI), clinical rehabilitation and cancer care, addressing the growing need for data-driven, personalized and technology-enabled healthcare solutions.
• Titolo autorizzato: AI-Driven Innovations in Physiotherapy and Oncology 3
• ISBN: 1-394-45222-5; 1-394-45219-5; 9781394452194
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911073921303321
• Lo trovi qui: Univ. Federico II
• Serie: ISTE Invoiced Series