| Autore |
Malviya Rishabha
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Newark : , : John Wiley & Sons, Incorporated, , 2024
|
| Descrizione fisica |
1 online resource (262 pages)
|
| Disciplina |
616.7/1028563
|
| Altri autori (Persone) |
RajputShivam
VaidyaMakarand
|
| Soggetto topico |
Bones - Diseases - Data processing
Artificial intelligence - Medical applications
|
| ISBN |
1-394-23091-5
1-394-23090-7
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Foreword -- Preface -- 1 Artificial Intelligence and Bone Fracture Detection: An Unexpected Alliance -- 1.1 Introduction -- 1.2 Bone Fracture -- 1.3 Deep Learning and Its Significance in Radiology -- 1.4 Role of AI in Bone Fracture Detection and Its Application -- 1.5 Primary Machine Learning-Based Algorithm in Bone Fracture Detection -- 1.6 Deep Learning-Based Techniques for Fracture Detection -- 1.7 Conclusion -- 2 Integrating AI With Tissue Engineering: The Next Step in Bone Regeneration -- 2.1 Introduction -- 2.2 Anatomy and Biology of Bone -- 2.3 Bone Regeneration Mechanism -- 2.4 Understanding AI -- 2.5 Current AI Integration -- 2.6 Applying Deep Learning -- 2.7 Conclusion -- 3 Deep Supervised Learning on Radiological Images to Classify Bone Fractures: A Novel Approach -- 3.1 Introduction -- 3.2 Common Bone Disorder -- 3.3 Deep Supervised Learning's Importance in Orthopedics and Radiology -- 3.4 Perspective From the Past -- 3.5 Essential Deep Learning Methods for Bone Imaging -- 3.6 Strategies for Effective Annotation -- 3.7 Application of Deep Learning to the Detection of Fractures -- 3.8 Conclusion -- 4 Treatment of Osteoporosis and the Use of Digital Health Intervention -- 4.1 Introduction -- 4.2 Opportunistic Diagnosis of Osteoporosis -- 4.3 Predictive Models -- 4.4 Assessment of Fracture Risk and Osteoporosis Diagnosis by Digital Health -- 4.5 Clinical Decision Support Tools, Reminders, and Prompts for Spotting Osteoporosis in Digital Health Settings -- 4.6 The Role of Digital Health in Facilitating Patient Education, Decision, and Conversation -- 4.7 Conclusion -- 5 Utilizing AI to Improve Orthopedic Care -- 5.1 Introduction -- 5.2 What is AI? -- 5.3 Introduction to Machine Learning: Algorithms and Applications -- 5.4 Natural Language Processing -- 5.5 The Internet of Things -- 5.6 Prospective AI Advantages in Orthopedics -- 5.7 Diagnostic Application of AI -- 5.8 Prediction Application With AI -- 5.9 Conclusion -- 6 Significance of Artificial Intelligence in Spinal Disorder Treatment -- 6.1 Introduction -- 6.2 Machine Learning -- 6.3 Methods Derived From Statistics -- 6.4 Applications of Machine Learning in Spine Surgery -- 6.5 Application of AI and ML in Spine Research -- 6.6 Conclusion -- 7 Osteoporosis Biomarker Identification and Use of Machine Learning in Osteoporosis Treatment -- 7.1 Introduction -- 7.2 Biomarkers of Bone Development -- 7.3 Biomarkers for Bone Resorption -- 7.4 Regulators of Bone Turnover -- 7.5 Methods to Identify Osteoporosis -- 7.6 Conclusion -- 8 The Role of AI in Pediatric Orthopedics -- 8.1 Introduction -- 8.2 Strategy Based on Artificial Intelligence -- 8.3 Several Applications of Artificial Intelligence -- 8.4 Conclusion -- 9 Use of Artificial Intelligence in Imaging for Bone Cancer -- 9.1 Introduction -- 9.2 Applications of Machine Learning to Cancer Diagnosis -- 9.3 Artificial Intelligence Methods for Diagnosing Bone Cancer -- 9.4 Methodologies for Constructing Deep Learning Model -- 9.5 Clinical Image Applications of Deep Learning for Bone Tumors -- 9.6 Conclusion -- References -- Index.
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| Record Nr. | UNINA-9911018786803321 |
Info
Advances and Insights into AI-Created Disability Supports / / by Rishabha Malviya, Shivam Rajput
