-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Machine Learning in Biomedical Science and Healthcare Informatics
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalArtificial Intelligence for Bone Disorder
 | Diagnosis and Treatment By Rishabha Malviya, Shivam Rajput and Markarand Vaidya Copyright: 2024 | Status: Published ISBN: 9781394230884 | Hardcover | 260 pages Price: $195 USD  |
One Line Description
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 learning algorithms, deep learning approaches, and their real-world uses.
Audience
This book will serve as a guide for orthopedic experts, biomedical engineers, faculty members, research scholars, IT specialists, healthcare workers, and hospital administrators
This book will serve as a guide for orthopedic experts, biomedical engineers, faculty members, research scholars, IT specialists, healthcare workers, and hospital administrators
Description
The book explores the use of artificial intelligence (AI) in the diagnosis and treatment of various bone illnesses. The integration of AI approaches in the fields of orthopedics, radiography, tissue engineering, and other areas related to bone are discussed in detail. It covers tissue engineering methods for bone regeneration and investigates the use of AI tools in this area, emphasizing the value of deep learning and how to use AI in tissue engineering efficiently.
The book also covers diagnostic and prognostic uses of AI in orthopedics, such as the diagnosis of disorders involving the hip and knee as well as prognoses for therapies. Chapters also look at MRI, trabecular biomechanical strength, and other methods for diagnosing osteoporosis. Other issues the book examines include several uses of AI in pediatric orthopedics, 3D modeling, digital X-ray radiogrammetry, convolutional neural networks for customized care, and digital tomography.
With information on the most recent developments and potential future applications, each chapter of the book advances our understanding of how AI might be used to diagnose and treat bone problems.
Back to Top
The book explores the use of artificial intelligence (AI) in the diagnosis and treatment of various bone illnesses. The integration of AI approaches in the fields of orthopedics, radiography, tissue engineering, and other areas related to bone are discussed in detail. It covers tissue engineering methods for bone regeneration and investigates the use of AI tools in this area, emphasizing the value of deep learning and how to use AI in tissue engineering efficiently.
The book also covers diagnostic and prognostic uses of AI in orthopedics, such as the diagnosis of disorders involving the hip and knee as well as prognoses for therapies. Chapters also look at MRI, trabecular biomechanical strength, and other methods for diagnosing osteoporosis. Other issues the book examines include several uses of AI in pediatric orthopedics, 3D modeling, digital X-ray radiogrammetry, convolutional neural networks for customized care, and digital tomography.
With information on the most recent developments and potential future applications, each chapter of the book advances our understanding of how AI might be used to diagnose and treat bone problems.
Back to Top
Author / Editor Details
Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. He has edited multiple volumes for Wiley-Scrivener.
Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. He has edited multiple volumes for Wiley-Scrivener.
Shivam Rajput, completed his MPharm. at Galgotias University, Greater Noida, India. He is currently an assistant professor at IITM College of Pharmacy, Sonipat, Hariyana, India. His areas of research include nanoformulations, cancer nanomedicine, and green nanotechnology for therapeutic applications.
Makarand Vaidya completed his Master of Surgery in Orthopaedics from Hindu Rao Hospital, Delhi where he is currently a faculty member. He has had a distinguished academic career and has published many research papers and books.
Back to Top
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
1.4.1 Data Pre-Processing
1.5 Primary Machine Learning-Based Algorithm in Bone Fracture Detection
1.5.1 Ensemble-Based Classification System
1.5.2 Bagging
1.5.3 Boosting
1.5.4 Stacking
1.5.5 Random Forest
1.6 Deep Learning-Based Techniques for Fracture Detection
1.6.1 R-CNN Series
1.6.2 YOLO Deep Learning Algorithm
1.6.3 YOLOv5
1.7 Conclusion
References
2. Integrating AI With Tissue Engineering: The Next Step in Bone Regeneration
2.1 Introduction
2.2 Anatomy and Biology of Bone
2.2.1 Bone Structure and Matrix
2.2.2 Bone Cells
2.3 Bone Regeneration Mechanism
2.3.1 Cellular Aspects of Bone Regeneration
2.3.2 Tissue Engineering for Bone Regeneration
2.3.3 Gene Therapy and Bone Regeneration
2.4 Understanding AI
2.5 Current AI Integration
2.6 Applying Deep Learning
2.6.1 Where Things Go Wrong
2.6.2 How to Integrate AI
2.7 Conclusion
References
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
3.5.1 Network Structure
3.5.2 Haar Wavelet Transforms
3.5.3 Scale-Invariant Feature Transform Algorithm
3.6 Strategies for Effective Annotation
3.7 Application of Deep Learning to the Detection of Fractures
3.7.1 Upper Limb Fractures
3.7.2 Lower Limb Fractures
3.7.3 Vertebral Fractures
3.8 Conclusion
References
4. Treatment of Osteoporosis and the Use of Digital Health Intervention
4.1 Introduction
4.2 Opportunistic Diagnosis of Osteoporosis
4.3 Predictive Models
4.3.1 Machine Learning and Deep Learning
4.3.2 The AI Algorithms
4.3.3 Treatment Decision Support
4.3.4 AI and Machine Learning in EHRs to Predict Falls and Fractures
4.4 Assessment of Fracture Risk and Osteoporosis Diagnosis by Digital Health
4.4.1 Advances in FLS Models and 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
4.6.1 Decision Tools for Patients and Shared Decision Making
4.6.2 Patient Interaction Enabled by Digital Health
4.6.3 Expert Clinical Instruction Aided by Digital Health Technologies
4.6.4 Digital Health-Supported Patient Education
4.6.5 Smart, Portable Applications for Smartphones
4.7 Conclusion
References
5. Utilizing AI to Improve Orthopedic Care
5.1 Introduction
5.2 What is AI?
5.3 Introduction to Machine Learning: Algorithms and Applications
5.3.1 Machine Learning Performance Indexes
5.3.2 Deep Learning
5.4 Natural Language Processing
5.5 The Internet of Things
5.6 Prospective AI Advantages in Orthopedics
5.7 Diagnostic Application of AI
5.7.1 The Diagnosis of Hip-Related Conditions
5.7.2 The Diagnosis of Knee-Related Conditions
5.7.3 Other Orthopedic-Related Diagnosis
5.8 Prediction Application With AI
5.8.1 Prediction Related to Surgery
5.8.2 Prediction Related to Post-Operative Complications
5.9 Conclusion
References
6. Significance of Artificial Intelligence in Spinal Disorder Treatment
6.1 Introduction
6.2 Machine Learning
6.3 Methods Derived From Statistics
6.3.1 Support Vector Machines
6.3.2 Classification and Regression Decision Trees
6.3.3 Artificial Neural Networks
6.3.4 CNN or ConvNets
6.3.5 Deep Learning
6.4 Applications of Machine Learning in Spine Surgery
6.4.1 Degenerative Disease
6.4.2 Spinal Deformity
6.4.3 Spine Trauma
6.4.4 Spine Oncology
6.5 Application of AI and ML in Spine Research
6.5.1 Spinal Structure Localization and Labeling
6.5.2 Segmentation
6.5.3 Diagnostic Imaging and Computer-Aided Diagnostics
6.5.4 Clinical Decision Support and Outcome Predictions
6.5.5 In-Image Search Based on Content
6.5.6 Biomechanics
6.5.7 Analyzing Motion and Gait
6.6 Conclusion
References
7. Osteoporosis Biomarker Identification and Use of Machine Learning in Osteoporosis Treatment
7.1 Introduction
7.2 Biomarkers of Bone Development
7.2.1 Total Alkaline Phosphatase
7.2.2 Bone-Specific Alkaline Phosphatase
7.2.3 Osteocalcin
7.2.4 Procollagen Type 1 N-Terminal Propeptide
7.2.5 Procollagen Type 1 C-Terminal Propeptide
7.3 Biomarkers for Bone Resorption
7.3.1 Hydroxyproline
7.3.2 Hydroxylysine
7.3.3 Deoxypyridinoline
7.3.4 Pyridinoline
7.3.5 Bone Sialoprotein
7.3.6 Osteopontin
7.3.7 Tartrate-Resistant Acid Phosphatase 5b
7.3.8 Carboxy-Terminal Crosslinked Telopeptide of Type 1 Collagen
7.3.9 Amino-Terminal Crosslinked Telopeptide of Type 1 Collagen
7.4 Regulators of Bone Turnover
7.4.1 Receptor Activator of NF-κB Ligand
7.4.2 Osteoprotegerin
7.4.3 Dickkopf-1
7.4.4 Sclerostin
7.5 Methods to Identify Osteoporosis
7.5.1 Two Sequences MRI
7.5.2 Trabecular Bio-Mechanical Strength
7.5.3 Bone Strength MRI
7.5.4 Radial Bias Function
7.5.5 Otsu Threshold Method
7.5.6 FDT-Based Techniques
7.5.7 3D Micro-MR Imaging
7.5.8 Fractal and GLCM Features
7.5.9 Fourier Transforms and Neural Networks
7.5.10 Geometric Features and Support Vector Regression
7.5.11 Dixon Magnetic Resonance Images
7.5.12 MRI-Based Three-Dimension
7.6 Conclusion
References
8. The Role of AI in Pediatric Orthopedics
8.1 Introduction
8.2 Strategy Based on Artificial Intelligence
8.2.1 Image Acquisition and Post-Processing
8.2.1.1 Quality of MRI Images and Scans
8.2.1.2 Improved CT Images and Lower Radiation Exposure
8.2.1.3 Uses in Contrast
8.2.2 Predictions and Quantitative Analysis
8.2.2.1 Quantification
8.2.2.2 Making Predictions With Models
8.2.3 Decoding the Images
8.2.3.1 Abnormality Detection and Categorization
8.2.4 Reporting
8.2.4.1 Prioritization of Workflow
8.2.4.2 Categorization of Pictures
8.2.5 Analysis of Outcomes and Management
8.2.5.1 Prominent Results
8.2.5.2 Improved Reports
8.3 Several Applications of Artificial Intelligence
8.3.1 Artificial Intelligence Software “BoneView”
8.3.2 Model Reconstruction in Three Dimensions
8.3.3 Digital X-Ray Radiogrammetry
8.3.4 Using Convolutional Neural Networks to Find, Categorize, and Individualize Treatments for Pediatric Cancer
8.3.5 Digital Tomosynthesis
8.3.6 Innovative 3D Printing Tools
8.3.7 Orthopedic Surgery with the Help of Robots
8.3.8 Digital Platform OrthoNext
8.4 Conclusion
References
9. Use of Artificial Intelligence in Imaging for Bone Cancer
9.1 Introduction
9.2 Applications of Machine Learning to Cancer Diagnosis
9.2.1 Decision Tree Algorithm
9.2.2 Support Vector Machine
9.2.3 Random Forests
9.2.4 Evolutionary Algorithms
9.2.5 Swarm Intelligence
9.3 Artificial Intelligence Methods for Diagnosing Bone Cancer
9.4 Methodologies for Constructing Deep Learning Model
9.4.1 Image Pre-Processing
9.4.2 Model Training
9.4.3 Model Performance Assessment
9.5 Clinical Image Applications of Deep Learning for Bone Tumors
9.5.1 Use of Deep Learning on Radiological Images to Diagnose Primary Bone Tumors
9.5.1.1 The Identification and Categorization of Lesions
9.5.1.2 Volume Calculation and Segmentation
9.5.1.3 Cancer Staging
9.5.1.4 Evaluation of the Pace of Tumor Necrosis
9.5.1.5 Prognosis Prediction
9.5.2 Deep Learning in Bone Metastasis
9.5.3 Use of Deep Learning on Pathology Images for Diagnosing Bone Cancer
9.6 Conclusion
References
Index
Back to Top
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
1.4.1 Data Pre-Processing
1.5 Primary Machine Learning-Based Algorithm in Bone Fracture Detection
1.5.1 Ensemble-Based Classification System
1.5.2 Bagging
1.5.3 Boosting
1.5.4 Stacking
1.5.5 Random Forest
1.6 Deep Learning-Based Techniques for Fracture Detection
1.6.1 R-CNN Series
1.6.2 YOLO Deep Learning Algorithm
1.6.3 YOLOv5
1.7 Conclusion
References
2. Integrating AI With Tissue Engineering: The Next Step in Bone Regeneration
2.1 Introduction
2.2 Anatomy and Biology of Bone
2.2.1 Bone Structure and Matrix
2.2.2 Bone Cells
2.3 Bone Regeneration Mechanism
2.3.1 Cellular Aspects of Bone Regeneration
2.3.2 Tissue Engineering for Bone Regeneration
2.3.3 Gene Therapy and Bone Regeneration
2.4 Understanding AI
2.5 Current AI Integration
2.6 Applying Deep Learning
2.6.1 Where Things Go Wrong
2.6.2 How to Integrate AI
2.7 Conclusion
References
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
3.5.1 Network Structure
3.5.2 Haar Wavelet Transforms
3.5.3 Scale-Invariant Feature Transform Algorithm
3.6 Strategies for Effective Annotation
3.7 Application of Deep Learning to the Detection of Fractures
3.7.1 Upper Limb Fractures
3.7.2 Lower Limb Fractures
3.7.3 Vertebral Fractures
3.8 Conclusion
References
4. Treatment of Osteoporosis and the Use of Digital Health Intervention
4.1 Introduction
4.2 Opportunistic Diagnosis of Osteoporosis
4.3 Predictive Models
4.3.1 Machine Learning and Deep Learning
4.3.2 The AI Algorithms
4.3.3 Treatment Decision Support
4.3.4 AI and Machine Learning in EHRs to Predict Falls and Fractures
4.4 Assessment of Fracture Risk and Osteoporosis Diagnosis by Digital Health
4.4.1 Advances in FLS Models and 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
4.6.1 Decision Tools for Patients and Shared Decision Making
4.6.2 Patient Interaction Enabled by Digital Health
4.6.3 Expert Clinical Instruction Aided by Digital Health Technologies
4.6.4 Digital Health-Supported Patient Education
4.6.5 Smart, Portable Applications for Smartphones
4.7 Conclusion
References
5. Utilizing AI to Improve Orthopedic Care
5.1 Introduction
5.2 What is AI?
5.3 Introduction to Machine Learning: Algorithms and Applications
5.3.1 Machine Learning Performance Indexes
5.3.2 Deep Learning
5.4 Natural Language Processing
5.5 The Internet of Things
5.6 Prospective AI Advantages in Orthopedics
5.7 Diagnostic Application of AI
5.7.1 The Diagnosis of Hip-Related Conditions
5.7.2 The Diagnosis of Knee-Related Conditions
5.7.3 Other Orthopedic-Related Diagnosis
5.8 Prediction Application With AI
5.8.1 Prediction Related to Surgery
5.8.2 Prediction Related to Post-Operative Complications
5.9 Conclusion
References
6. Significance of Artificial Intelligence in Spinal Disorder Treatment
6.1 Introduction
6.2 Machine Learning
6.3 Methods Derived From Statistics
6.3.1 Support Vector Machines
6.3.2 Classification and Regression Decision Trees
6.3.3 Artificial Neural Networks
6.3.4 CNN or ConvNets
6.3.5 Deep Learning
6.4 Applications of Machine Learning in Spine Surgery
6.4.1 Degenerative Disease
6.4.2 Spinal Deformity
6.4.3 Spine Trauma
6.4.4 Spine Oncology
6.5 Application of AI and ML in Spine Research
6.5.1 Spinal Structure Localization and Labeling
6.5.2 Segmentation
6.5.3 Diagnostic Imaging and Computer-Aided Diagnostics
6.5.4 Clinical Decision Support and Outcome Predictions
6.5.5 In-Image Search Based on Content
6.5.6 Biomechanics
6.5.7 Analyzing Motion and Gait
6.6 Conclusion
References
7. Osteoporosis Biomarker Identification and Use of Machine Learning in Osteoporosis Treatment
7.1 Introduction
7.2 Biomarkers of Bone Development
7.2.1 Total Alkaline Phosphatase
7.2.2 Bone-Specific Alkaline Phosphatase
7.2.3 Osteocalcin
7.2.4 Procollagen Type 1 N-Terminal Propeptide
7.2.5 Procollagen Type 1 C-Terminal Propeptide
7.3 Biomarkers for Bone Resorption
7.3.1 Hydroxyproline
7.3.2 Hydroxylysine
7.3.3 Deoxypyridinoline
7.3.4 Pyridinoline
7.3.5 Bone Sialoprotein
7.3.6 Osteopontin
7.3.7 Tartrate-Resistant Acid Phosphatase 5b
7.3.8 Carboxy-Terminal Crosslinked Telopeptide of Type 1 Collagen
7.3.9 Amino-Terminal Crosslinked Telopeptide of Type 1 Collagen
7.4 Regulators of Bone Turnover
7.4.1 Receptor Activator of NF-κB Ligand
7.4.2 Osteoprotegerin
7.4.3 Dickkopf-1
7.4.4 Sclerostin
7.5 Methods to Identify Osteoporosis
7.5.1 Two Sequences MRI
7.5.2 Trabecular Bio-Mechanical Strength
7.5.3 Bone Strength MRI
7.5.4 Radial Bias Function
7.5.5 Otsu Threshold Method
7.5.6 FDT-Based Techniques
7.5.7 3D Micro-MR Imaging
7.5.8 Fractal and GLCM Features
7.5.9 Fourier Transforms and Neural Networks
7.5.10 Geometric Features and Support Vector Regression
7.5.11 Dixon Magnetic Resonance Images
7.5.12 MRI-Based Three-Dimension
7.6 Conclusion
References
8. The Role of AI in Pediatric Orthopedics
8.1 Introduction
8.2 Strategy Based on Artificial Intelligence
8.2.1 Image Acquisition and Post-Processing
8.2.1.1 Quality of MRI Images and Scans
8.2.1.2 Improved CT Images and Lower Radiation Exposure
8.2.1.3 Uses in Contrast
8.2.2 Predictions and Quantitative Analysis
8.2.2.1 Quantification
8.2.2.2 Making Predictions With Models
8.2.3 Decoding the Images
8.2.3.1 Abnormality Detection and Categorization
8.2.4 Reporting
8.2.4.1 Prioritization of Workflow
8.2.4.2 Categorization of Pictures
8.2.5 Analysis of Outcomes and Management
8.2.5.1 Prominent Results
8.2.5.2 Improved Reports
8.3 Several Applications of Artificial Intelligence
8.3.1 Artificial Intelligence Software “BoneView”
8.3.2 Model Reconstruction in Three Dimensions
8.3.3 Digital X-Ray Radiogrammetry
8.3.4 Using Convolutional Neural Networks to Find, Categorize, and Individualize Treatments for Pediatric Cancer
8.3.5 Digital Tomosynthesis
8.3.6 Innovative 3D Printing Tools
8.3.7 Orthopedic Surgery with the Help of Robots
8.3.8 Digital Platform OrthoNext
8.4 Conclusion
References
9. Use of Artificial Intelligence in Imaging for Bone Cancer
9.1 Introduction
9.2 Applications of Machine Learning to Cancer Diagnosis
9.2.1 Decision Tree Algorithm
9.2.2 Support Vector Machine
9.2.3 Random Forests
9.2.4 Evolutionary Algorithms
9.2.5 Swarm Intelligence
9.3 Artificial Intelligence Methods for Diagnosing Bone Cancer
9.4 Methodologies for Constructing Deep Learning Model
9.4.1 Image Pre-Processing
9.4.2 Model Training
9.4.3 Model Performance Assessment
9.5 Clinical Image Applications of Deep Learning for Bone Tumors
9.5.1 Use of Deep Learning on Radiological Images to Diagnose Primary Bone Tumors
9.5.1.1 The Identification and Categorization of Lesions
9.5.1.2 Volume Calculation and Segmentation
9.5.1.3 Cancer Staging
9.5.1.4 Evaluation of the Pace of Tumor Necrosis
9.5.1.5 Prognosis Prediction
9.5.2 Deep Learning in Bone Metastasis
9.5.3 Use of Deep Learning on Pathology Images for Diagnosing Bone Cancer
9.6 Conclusion
References
Index
Back to Top