Artificial Intelligence for Bone Disorder : Diagnosis and Treatment

Artificial Intelligence for Bone Disorder Diagnosis and Treatment

ARTIFICIAL INTELLIGENCE FOR BONE DISORDER The authors have produced an invaluable resource that connects the fields of AI and bone treatment by providing essential insights into the current state and future of AI in bone condition diagnosis and therapy, as well as a methodical examination of machine...

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Bibliographic Details
Main Author: Malviya, Rishabha (-)
Other Authors: Rajput, Shivam, Vaidya, Makarand
Format: eBook
Language:Inglés
Published: Newark : John Wiley & Sons, Incorporated 2024.
Edition:1st ed
Subjects:
Bones > Diseases > Data processing.
Artificial intelligence > Medical applications.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828035306719
Table of Contents:
  • 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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