Biomedical Visualisation: Volume 11 (Advances in Experimental Medicine and Biology, 1356)

This edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will also be able to learn about the use of visualisation techniques and technologies for the historical and forensic settings. The chapters presented in this volume cover such a diverse range of topics, with something for everyone. We present here chapters on 3D visualising novel stent grafts to aid treatment of aortic aneuryms; confocal microscopy constructed vascular models in patient education; 3D patient specific virtual reconstructions in surgery; virtual reality in upper limb rehabilitation in patients with multiple sclerosis and virtual clinical wards. In addition, we present chapters in artificial intelligence in ultrasoundguided regional anaesthesia; carpal tunnel release visualisation techniques; visualising for embryology education and artificial intelligence data on bone mechanics. Finally we conclude with chapters on visualising patient communication in a general practice setting; digital facial depictions of people from the past; instructor made cadaveric videos, novel cadaveric techniques for enhancing visualisation of the human body and finally interactive educational videos and screencasts. This book explores the use of technologies from a range of fields to provide engaging and meaningful visual representations of the biomedical sciences. It is therefore an interesting read for researchers, developers and educators who want to learn how visualisation techniques can be used successfully for a variety of purposes, such as educating students or training staff, interacting with patients and biomedical procedures in general. 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 Correction to: How Artificial Intelligence and Machine Learning Is Assisting Us to Extract Meaning from Data on Bone Mechanics? Correction to: Chapter 9 in: P. M. Rea (ed.), Biomedical Visualisation, Advances in Experimental Medicine and Biology 1356, ht...