Biomedical Visualisation: Volume 11

Preface
Acknowledgements
About the Book
Contents
Editor and Contributors
1: Creating Interactive Three-Dimensional Applications to Visualise Novel Stent Grafts That Aid in the Treatment of Aortic Ane...
1.1 Introduction
1.2 Background
1.2.1 Aortic Aneurysm Background
1.2.1.1 Thoracic Aortic Aneurysms
1.2.1.2 Abdominal Aortic Aneurysms
1.2.2 Surgical Interventions for AAAs and TAAs
1.2.2.1 Open Surgical Repair and Endovascular Aneurysm Repair of AAAs
1.2.2.2 Open Surgical Repair and Endovascular Aneurysm Repair of TAAs
1.2.3 Potential of Medical Visualisations for Surgical Techniques
1.2.3.1 Imaging Modalities in a Healthcare Setting
1.2.3.2 Public Engagement for Medical Visualisation
1.3 Methods
1.3.1 Conceptual Development (Storyboard/Outline)
1.3.2 Digital 3D Content Production
1.3.2.1 Segmentation of the Aorta, Kidneys and Associated Vessels
1.3.2.2 Bifrost Visual Programming
1.3.2.2.1 Voxel Volume Remeshing Using Bifrost Graph Editor
1.3.2.3 Retopology and Sculpting
1.3.2.4 Modelling of the Heart
1.3.2.5 Modelling of Relay Endograft
1.3.2.6 Modelling of Fenestrated Anaconda Endograft
1.3.2.6.1 Wires and Stitching of Stent Graft
1.3.2.6.2 Stitches and Fine Details of Graft
1.3.2.6.3 Additional Stent Body Models
1.3.2.6.4 Deployment Devices
1.3.2.7 Texturing in Substance Painter
1.3.2.8 Informational Animations
1.3.2.8.1 Animations for the Fenestrated Anaconda Stent Graft
1.3.2.8.2 Animations for the Proximal Relay Stent Graft
1.3.2.8.3 Red Blood Cell Flow Animations
1.3.2.8.4 Post Processing
1.3.2.9 Application Development
1.3.2.9.1 Home Screen
1.3.2.9.2 Features Section
1.3.2.9.3 Clinical Performance and Deployment Sections
1.4 Results
1.4.1 Outcomes from Evaluating the Finished Application with Clinical Professionals
1.5 Discussion
1.5.1 Discussion of Development Process
1.5.2 Discussion of Application Feedback
1.5.3 Benefits and Drawbacks of the Application/3D Visualisation Technique
1.5.4 Limitations
1.5.5 Further Development
1.6 Conclusion
References
2: Using Confocal Microscopy to Generate an Accurate Vascular Model for Use in Patient Education Animation
2.1 Introduction
2.2 Blood Pressure
2.3 Blood Pressure Regulation
2.4 Pathophysiology of Hypertension
2.5 Peripheral Resistance Artery Structure and Vascular Remodelling in Hypertension
2.6 Treatment of Hypertension
2.7 Medication Adherence
2.8 Patient Education Can Improve Medication Adherence
2.9 Generating Digital 3D Models Using Confocal Microscopy
2.10 Building a Complete Vessel 3D Model from a Partial Confocal Microscopy Dataset
2.11 Modelling the Tunica Intima
2.12 Tunica Media
2.13 Tunica Externa
2.14 Simple Effects in Animation
2.15 Vascular Wall Remodelling Using Blend Shapes
2.16 Maya´s MASH Toolkit
2.17 Materials (Shaders)
2.18 Lighting
2.19 Rendering
2.20 Results
2.21 Discussion and Evaluation
References
3: Methods and Applications of 3D Patient-Specific Virtual Reconstructions in Surgery
3.1 Introduction
3.2 Methods of 3D Virtual Reconstructions
3.2.1 Segmentation
3.2.1.1 Manual Segmentation
3.2.1.2 Algorithmic Approaches to Segmentation
3.2.2 Rendering Methods for 3D Virtual Models
3.2.2.1 Volumetric Rendering
3.2.2.2 Surface Rendering Techniques
3.2.3 Post-Processing of Surface Polygon Mesh
3.2.3.1 Decimation
3.2.3.2 Smoothing
3.2.4 Advanced 3D Modelling Techniques
3.2.4.1 Complex 3D Modelling and Digital Sculpture
3.2.4.2 Retopology
3.2.4.3 UV Unwrapping
3.2.4.4 Texture Maps and Physically Based Rendering
3.3 Applications of 3D Models in Surgical Practice
3.3.1 3D Models in Surgical Planning
3.3.1.1 Anatomical Understanding
3.3.1.2 Patient-Specific Simulation
3.3.1.3 Resection Planning
3.3.1.4 Reconstruction
3.3.2 Intraoperative Navigation
3.3.3 3D Models in Surgical Patient Education
3.4 Conclusion
References
4: Proof of Concept for the Use of Immersive Virtual Reality in Upper Limb Rehabilitation of Multiple Sclerosis Patients
4.1 Rationale
4.2 Multiple Sclerosis and Conventional Physiotherapy
4.3 Virtual Reality-Based Rehabilitation
4.3.1 Interaction
4.3.2 Visualisation
4.3.3 HMDs in MS Rehabilitation
4.4 Treatment Adherence and Motivation
4.4.1 Feedback
4.5 Aims and Objectives
4.6 Methods
4.6.1 Workflow (Fig. 4.1)
4.6.1.1 Materials
4.6.2 Design and Development Process
4.7 Developmental Outcomes
4.7.1 Menu Scene
4.7.2 Piano Scene
4.7.3 Maze Scene
4.7.4 Evaluation
4.7.4.1 Participants
4.7.4.2 Experimental Set-Up and Procedure
4.7.4.3 Ethics
4.7.4.4 Data Analysis
4.8 Results
4.9 Discussion
4.9.1 Future Works
4.10 Conclusion
References
5: Virtual Wards: A Rapid Adaptation to Clinical Attachments in MBChB During the COVID-19 Pandemic
5.1 Introduction
5.2 Theoretical Underpinnings
5.2.1 Dual-Process Theory
5.2.2 Script Theory
5.2.3 Cognitive Load Theory
5.2.4 Situated Cognition
5.3 Technological Considerations
5.3.1 Flexibility of Content
5.3.2 Inclusion of Automatically Marked Questions
5.3.3 Control over Non-linear Lesson Flow
5.3.4 Large Amount of Information in a Single Click
5.3.5 Embedding H5G Interactive Content
5.3.6 Tips for Virtual Ward Developers
5.4 Description of the Virtual Wards
5.4.1 The Content Covered by the Virtual Wards
5.4.2 The Format of the Modules
5.4.3 The Interactive Cases
5.4.3.1 Setting the Scene
5.4.3.2 Interactive History-Taking
5.4.3.3 Observations and Examination
5.4.3.4 Investigations: Selection and Interpretation
5.4.3.5 Refining the Differential
5.4.3.6 Management
5.5 Evaluation and Future
5.5.1 Asynchronous Engagement with Virtual Wards
5.5.2 Issues Working with Multiple New Technologies
5.5.3 Clinician Time Involved to Create Content
5.5.4 Simultaneous Virtual Wards
5.5.5 Quality Control of Benevolent Contributor Content
5.5.6 A Reflection on the Faculty Experience
5.5.7 The Students´ Perspective
5.5.7.1 The Virtual Ward Format
5.5.7.2 Feedback on Content
5.5.7.3 Amount of Content
5.5.7.4 Technical Difficulties
5.5.7.5 Loss of Clinical Contact
5.5.8 Lessons Learnt
5.6 Tips for Setting Up Virtual Wards
5.7 The Future of Virtual Wards
References
6: Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthe...
6.1 Introduction
6.2 Part 1: Challenges in Ultrasound Image Interpretation and Ultrasound-Guided Regional Anaesthesia
6.2.1 What Is Ultrasound-Guided Regional Anaesthesia?
6.2.2 Why Is Regional Anaesthesia Difficult?
6.2.2.1 Selection of the Right Block
6.2.2.2 Acquiring and Interpreting an Optimised Ultrasound Image
6.2.2.2.1 Operator Dependence
6.2.2.2.2 Anatomical Variation
6.2.2.2.3 Learning Materials Depict Ideal Versions of Sono-anatomy
6.2.2.2.4 Comorbidity
6.2.2.2.5 Inattentional Blindness
6.2.2.2.6 Satisfaction of Search
6.2.2.2.7 Fatigability
6.2.2.3 Planning a Safe Needle Path and Visualising the Needle Tip
6.2.2.4 Ensuring Accurate Deposition of Local Anaesthetic Around the Target Structure
6.2.2.5 Post-Procedure Monitoring Both to Ensure Effect and to Monitor for any Complications
6.2.3 Education in Ultrasound-Guided Regional Anaesthesia
6.3 Part 2: An Introduction to Artificial Intelligence for Clinicians
6.3.1 What Is Artificial Intelligence?
6.3.2 Machine Learning Categories
6.3.3 The Computational Problem
6.3.4 Rule-Based vs Model-Based Techniques
6.3.4.1 Rule-Based Techniques
6.3.4.2 Model-Based Techniques
6.3.5 Convolutional Neural Networks
6.3.6 The U-Net Architecture
6.3.7 How Models Train
6.3.8 Model Evaluation
6.4 Part 3: The Current State of AI in Ultrasound Image Interpretation for Ultrasound-Guided Regional Anaesthesia
6.4.1 How Can Technology Be Used to Augment UGRA?
6.4.2 Summary of Different Approaches
6.4.3 Segmentation
6.4.3.1 Deep Learning Approaches
6.4.3.2 Non-deep Learning Approaches
6.4.4 Tracking Methods
6.4.4.1 How Does Tracking Fit in with Segmentation?
6.4.4.2 Approaches
6.4.5 Summary and Future Directions
6.5 Part 4: A Case Study: ScanNav Anatomy Peripheral Nerve Block
6.6 Part 5: The Future: Artificial Intelligence and Ultrasound-Guided Regional Anaesthesia
6.6.1 Supporting Practice
6.6.2 Changing How We Learn
6.6.3 The Extra Dimension
6.6.4 The Future of Clinical Practice
References
7: A Systematic Review of Randomised Control Trials Evaluating the Efficacy and Safety of Open and Endoscopic Carpal Tunnel Re...
7.1 Introduction
7.1.1 Carpal Tunnel Syndrome
7.1.2 The Surgical Interventions
7.1.3 Aims and Objectives
7.2 Methods
7.2.1 Study Identification
7.2.2 Study Screening and Selection
7.2.3 Assessment of Patient Outcomes
7.2.4 Risk of Bias Assessment
7.2.5 Data Analysis
7.3 Results
7.3.1 Study Identification, Screening and Inclusion
7.3.2 Study Characteristics
7.3.3 Patient Outcomes
7.3.4 Risk of Bias Assessment
7.4 Discussion
7.4.1 Main Findings
7.4.2 Study Quality
7.4.3 Limitations
7.4.4 Conclusions
Appendices
Appendix 1. Table of Individual Participant and Study Characteristics
Appendix 2. Table of Participant Outcome Assessment
Appendix 3. Table of Individual Study Bias Assessment
Appendix 4. Characteristics of Excluded Studies
References
Included References
Excluded References
Additional References
8: Exploring Visualisation for Embryology Education: A Twenty-First-Century Perspective
8.1 Introduction
8.2 History of Visualisation in Embryology and Challenges in the Twenty-First Century
8.2.1 In the Nineteenth Century
8.2.2 In the Twentieth Century
8.2.3 In the Twenty-First Century
8.2.3.1 Challenges in Embryology Teaching in the Twenty-First Century
8.3 Learning Theories
8.3.1 Cognitive Load Theory
8.4 Current Resources in Embryology Teaching
8.4.1 Videos and YouTube
8.4.2 Animations
8.4.3 Virtual Reality
8.4.4 Virtual Dissection Tables
8.5 Summary of Evidence-Based Studies on Using Visualisation in Embryology Teaching
8.6 Case Study: Integrating 3D Embryology Learning Resources Within a Medical School Curriculum
8.6.1 Educational Context
8.6.1.1 Pedagogical Basis
8.6.1.2 Pre-pandemic Curriculum
8.6.1.3 Post-Pandemic Curriculum
8.6.2 Pre-Covid-19 Innovations for Embryology Learning
8.6.2.1 Social Media and Creative Art-Based Approaches
8.6.2.2 Development of a Prototype Digital Embryology Resource
8.6.3 Approaches to Asynchronous Embryology Education During Covid-19
8.6.3.1 Integrated Embryology VLE Tutorial
8.6.3.2 HDBR Atlas
8.6.3.3 Three-Dimensional Atlas of Human Embryology
8.7 Conclusion
References
9: How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics?
9.1 An Introduction to the Book Chapter
9.2 The Applications of Artificial Intelligence and Machine Learning to Bone Mechanics Research
9.2.1 What Are Artificial Intelligence and Machine Learning?
9.2.2 Richness and Abundance of Data as Well as Powerful Computational Tools Motivate the Application of ML in Bone Mechanics
9.2.3 Main Areas of Bone Mechanics Where Machine Learning Is Worth-Employing
9.3 Machine Learning Algorithms
9.3.1 Types of Machine Learning Based on Learning Paradigm
9.3.2 Main Steps Involved in Machine Learning
9.3.3 Performance Metrics
9.3.4 Training Algorithm
9.3.5 Training, Validation, and Testing Datasets
9.4 Artificial Neural Networks
9.5 Applications of Artificial Neural Networks to Bone Mechanics
9.6 Perspectives, Conclusions, and Future Directions
References
10: Visual Communication and Creative Processes Within the Primary Care Consultation
10.1 Ethics
10.2 Combining Visual Communication and the Medical Consultation
10.3 Medical Consultation Models
10.4 Examples of Illustration and Visual Communication in my GP Consultations
10.5 Congestive Heart Failure
10.6 Ear Nose and Throat Conditions
10.7 Analogy and Metaphor
10.8 Patient Drawings and Pain
10.9 Diabetes
10.10 Gynaecology
10.11 Urology
10.12 Colour
10.13 Inclusive Visual Communication in Dementia, Autistic Spectrum Disorder and Learning Disability
10.14 Art Therapy and Use of Allegory
10.15 Summary
References
11: Digital 2D, 2.5D and 3D Methods for Adding Photo-Realistic Textures to 3D Facial Depictions of People from the Past
11.1 Introduction
11.1.1 Existing Facial Reconstruction Methods
11.1.2 What Is the Purpose of a Facial Depiction?
11.2 3D Digital Texture Methods
11.2.1 2D Digital Composite Method
11.2.1.1 Workflow
11.2.2 3D Digital Painting and Rendering Method
11.2.2.1 Workflow
11.2.3 2.5D Digital Composite Method
11.2.3.1 Workflow
11.3 Discussion
11.3.1 Comparing Methods for Adding Digital Textures to 3D Facial Reconstructions
11.3.2 Artistic Proficiency and Cognitive Biases
11.4 Conclusion
References
12: Teaching with Cadavers Outside of the Dissection Room Using Cadaveric Videos
12.1 Introduction
12.1.1 Transition During Covid-19
12.1.2 Anatomy at Brighton and Sussex Medical School
12.2 Cadaveric Videos
12.2.1 Student Opinion
12.2.2 Learning Gain
12.2.3 Engagement
12.3 Cognitive Load Theory
12.3.1 Split Attention and Modality Effects
12.3.2 Task Complexity and Self-Efficacy
12.3.3 Task Fidelity and Affect
12.4 Sharing Cadaveric Images Online with Students
12.4.1 Opportunity to Develop Digital Professionalism and Fluency
12.4.2 Storage of Cadaveric Images
12.4.3 Existing Online Learning Resources Versus Bespoke Cadaveric Video Content
12.5 Conclusion
References
13: A Novel Cadaveric Embalming Technique for Enhancing Visualisation of Human Anatomy
13.1 History of Embalming
13.2 Modern Approaches to Cadaveric Preservation
13.2.1 Phenol
13.2.2 Formaldehyde
13.2.3 Thiel
13.2.4 Alternative Fixatives
13.2.5 Fresh-Frozen Preservation
13.3 Learning and Teaching with Cadavers
13.3.1 Curricular Integration
13.3.2 Visualisation, Sensation and Emotion
13.4 The Newcastle Experience
13.4.1 Educational and Technical Context
13.4.2 Newcastle Formaldehyde-Phenol Mix Embalming
13.4.3 Newcastle-WhitWell Embalming Protocol
13.5 Summary, Conclusions, and Implications for Practice
References
14: Assessing the Impact of Interactive Educational Videos and Screencasts Within Pre-clinical Microanatomy and Medical Physio...
14.1 Introduction
14.1.1 The Use of Video in Clinical Anatomy
14.1.2 The Use of Screencasts in Clinical Anatomy
14.1.3 The Use of Interactive Video in Anatomy Education
14.1.4 How Do These Resources Improve Learning?
14.1.4.1 The Spatial-Contiguity Principle
14.1.4.2 The Temporal Contiguity Principle
14.1.4.3 The Modality Principle
14.1.5 Methods for Assessing Learning Gain
14.1.6 Rationale for a Study in Basic Medical Sciences
14.2 Aims
14.3 Methods
14.3.1 Video Production and Design
14.3.2 Participant Recruitment
14.3.3 Knowledge Testing
14.4 Results
14.4.1 Demographics
14.4.2 Knowledge Testing
14.4.2.1 Histology Resources: Learning Gain and Retention
14.4.2.2 Pain Physiology Resources: Learning Gain and Retention
14.4.3 Student Attitudes, Perceptions, and User Experience
14.4.3.1 Standalone Questions: Post-teaching Perceptions
14.4.3.2 Histology Perceptions: Paired Questions
14.4.3.3 Physiology Perceptions: Paired Questions
14.4.4 Comparison Between Cohorts
14.4.4.1 Perceived Confidence
14.5 Discussion
14.5.1 Introduction
14.5.2 Learning Gain and Knowledge Retention
14.5.3 Assessing Interactive Video
14.5.4 Curriculum Integration of Video Resources
14.5.5 Student Perceptions and Preferences
14.5.6 Screencasts and Standard Video Formats
14.5.7 The Learner Experience
14.5.8 Methodological Approaches
14.5.9 Limitations
14.5.10 Future Work
14.6 Conclusion
References