Biomedical Information Technology (Biomedical Engineering)

Cover
Biomedical Information Technology
Copyright
Contributors
Acknowledgements
Introduction
Part One: Biomedical data technologies
ONE . Medical imaging
	1.1 Introduction
	1.2 Digital radiography
		1.2.1 Formation and characteristics of X-rays
		1.2.2 Scatter and attenuation of X-rays in tissue
		1.2.3 Instrumentation for digital radiography
	1.3 Computed tomography
		1.3.1 Principles of computed tomography
		1.3.2 Spiral and multislice computed tomography
	1.4 Nuclear medicine
		1.4.1 Radioactive nuclides in nuclear medicine
		1.4.2 Nuclear medicine detectors
		1.4.3 Single-photon emission-computed tomography
		1.4.4 Positron-emission tomography
		1.4.5 Combined positron-emission tomography/computed tomography scanners
		1.4.6 Combined positron-emission tomography/magnetic resonance scanners
	1.5 Ultrasonic imaging
		1.5.1 Fundamentals of ultrasound
		1.5.2 Transducers and beam characteristics
		1.5.3 Image acquisition and display
	1.6 Magnetic resonance imaging
		1.6.1 Basis of magnetic resonance imaging
		1.6.2 Magnetic field gradients
		1.6.3 Fourier imaging techniques
		1.6.4 Magnetic resonance imaging contrast agents
	1.7 Diffuse optical imaging
		1.7.1 Propagation of light through tissue
		1.7.2 Measurement of blood oxygenation
		1.7.3 Image reconstruction
		1.7.4 Measurement techniques
	1.8 Biosignals
		1.8.1 Electroencephalography
		1.8.2 Electrocardiograms
	1.9 Digital cameras and microscopes
	Appendix
		A.1 Fourier transforms
		A.2 Filtered backprojection
		A.3 Iterative image reconstruction
	Exercises
	Further reading
	General imaging textbooks
	X-ray and computed tomography books
	Nuclear medicine books
	Ultrasonic imaging
	Magnetic resonance imaging
	Diffuse optical imaging books
	Diffuse optical imaging review papers
	Biosignals
	Digital cameras and microscopes
TWO . Biomedical sensors
	2.1 Introduction
	2.2 Wearable devices
		2.2.1 Wearable sensing technology and needs
		2.2.2 Application examples of wearable sensing technology
			2.2.2.1 Microelectromechanical system motion sensor
			2.2.2.2 Flexible sensor
			2.2.2.3 Wearable biosensors
		2.2.3 GluSense artificial islet system
		2.2.4 Contact lenses for detecting blood sugar
	2.3 Biochip
		2.3.1 Gene chips
		2.3.2 Protein chips
		2.3.3 Cell chips
		2.3.4 Tissue chips
		2.3.5 Organoid chips
	2.4 Biosensors
		2.4.1 Biological molecular sensor
		2.4.2 Cell-based biosensors
	2.5 Implantable sensors
		2.5.1 Biocompatibility
		2.5.2 Biofunctionality: sensitivity and specificity
		2.5.3 Miniaturizing: nanomaterials
		2.5.4 Lifetime
	2.6 Neural sensing and interfacing
	2.7 Summary
	References
THREE . Biological computing
	3.1 Introduction
	3.2 General workflow for the analysis of biological samples
	3.3 Overview of genomic methods
		3.3.1 Introduction of next-generation DNA sequencing
		3.3.2 Workflow for DNA sequencing data processing
		3.3.3 Other types of sequencing data and applications
	3.4 Overview of proteomic methods
		3.4.1 Noise filtering
		3.4.2 Deisotoping
		3.4.3 Peak detection
		3.4.4 Normalization
		3.4.5 Retention time alignment and peak matching
		3.4.6 Differential expression analysis
		3.4.7 Analysis of targeted quantitative proteomic data
		3.4.8 Introduction to label-based protein quantitation
		3.4.9 Introduction of protein data processing pipeline of MS-PyCloud
	3.5 Biological databases and open-source software
		3.5.1 Brief introduction of major biological databases
		3.5.2 Introduction of open-source software
		3.5.3 Usability of open source software
		3.5.4 Commercial products based on open-source software
	3.6 Biological network analysis
		3.6.1 Brief introduction of biological network analysis
		3.6.2 Introduction of differential dependency network analysis
	3.7 Summary
	Acknowledgments
	References
FOUR . Picture archiving and communication systems and electronic medical records for the healthcare enterprise
	4.1 Introduction
		4.1.1 The role of the picture archiving and communication system in the clinical environment
		4.1.2 The role of the picture archiving and communication system in medical imaging informatics
		4.1.3 General picture archiving and communication system design: introduction and impact
		4.1.4 Chapter overview
	4.2 Picture archiving and communication system infrastructure
		4.2.1 Introduction to picture archiving and communication system infrastructure design
		4.2.2 Industry standards
			4.2.2.1 Health Level 7
			4.2.2.2 Digital Imaging and Communications in Medicine version 3.0 standard
			4.2.2.3 Digital Imaging and Communications in Medicine data model
			4.2.2.4 Digital Imaging and Communications in Medicine service classes
			4.2.2.5 Integrating the Healthcare Enterprise
		4.2.3 Connectivity and open architecture
		4.2.4 Reliability
		4.2.5 Security
		4.2.6 Current picture archiving and communication system architectures
			4.2.6.1 Client/server picture archiving and communication system architecture
			4.2.6.2 Web-based model
	4.3 Picture archiving and communication system components and workflow
		4.3.1 Introduction of components
		4.3.2 Image acquisition gateway
		4.3.3 Picture archiving and communication system server and image archive
		4.3.4 Display workstations
		4.3.5 Communications and networking
		4.3.6 Picture archiving and communication system workflow
	4.4 Picture archiving and communication system server and image archive
		4.4.1 Image management and design concept
		4.4.2 Picture archiving and communication system server and storage archive functions
			4.4.2.1 The archive server
			4.4.2.2 The database system
			4.4.2.3 The storage archive or library
			4.4.2.4 Communication networks
			4.4.2.5 Picture archiving and communication system server and storage archive functions
		4.4.3 Digital Imaging and Communications in Medicine–compliant picture archiving and communication system archive server
		4.4.4 Hardware and software components
			4.4.4.1 Redundant array of inexpensive disks
			4.4.4.2 Digital linear tape
			4.4.4.3 Storage area network
			4.4.4.4 Cloud storage
			4.4.4.5 Vendor neutral archive
			4.4.4.6 Archive server software
		4.4.5 Disaster recovery and backup archive solutions
		4.4.6 Current changes in picture archiving and communication system architecture: the vendor neutral archive
	4.5 Picture archiving and communication system clinical experiences
		4.5.1 Introduction
		4.5.2 Picture archiving and communication system implementation strategy
			4.5.2.1 Risk assessment analysis
			4.5.2.2 Implementation phase development
			4.5.2.3 Development of workgroups
			4.5.2.4 Implementation management
		4.5.3 System acceptance
		4.5.4 Image/data migration
		4.5.5 Picture archiving and communication system clinical experiences and pitfalls
			4.5.5.1 Clinical experiences at Baltimore VA Medical Center
			4.5.5.2 Clinical experience at Saint John's Health Center
			4.5.5.3 Picture archiving and communication system pitfalls
	4.6 Introduction to hospital clinical systems
		4.6.1 Hospital information system and the electronic medical record
		4.6.2 Radiology information system
		4.6.3 Voice recognition system
		4.6.4 Interfacing picture archiving and communication, hospital information, radiology information, and voice recognition systems ...
			4.6.4.1 Database-to-database transfer
			4.6.4.2 Interface engine
			4.6.4.3 Integrating health information, radiology information, picture archiving and communication, and voice recognition systems
	4.7 Picture archiving and communication systems and electronic medical records
		4.7.1 Changes in the roles of the picture archiving and communication systems and electronic medical records in healthcare
		4.7.2 Large-scale enterprise-wide electronic medical record implementation and design
			4.7.2.1 Step 1: strategic planning
			4.7.2.2 Step 2: adapting the workflow
			4.7.2.3 Step 3: financing
			4.7.2.4 Step 4: recruiting the workforce
			4.7.2.5 Step 5: collaboration
			4.7.2.6 Step 6: choosing an electronic medical record vendor
			4.7.2.7 Step 7: go-live and preparation for clinical use
				4.7.2.7.1 Data migration and cleansing
				4.7.2.7.2 Training program development
				4.7.2.7.3 Go-live activities
			4.7.2.8 Step 8: system evaluation and optimizing for quality assessment
		4.7.3 Electronic medical record integration with medical images and picture archiving and communication system
		4.7.4 Electronic medical record implementation use case: Los Angeles County department of Health Services ORCHID project
			4.7.4.1 Integration of ORCHID with picture archiving and communication system and non-DICOM images
	4.8 Summary
	4.9 Exercises
	Further reading
Part Two: Artificial intelligence and big data processing in biomedicine
FIVE
. Machine learning in medical imaging
	5.1 Medical imaging
		5.1.1 Role in healthcare
	5.2 Machine intelligence and machine learning
	5.3 Supervised learning
		5.3.1 Overview
		5.3.2 Classification with supervised machine learning
			5.3.2.1 Nearest neighbor approaches
			5.3.2.2 Support vector machines
			5.3.2.3 Supervised deep learning
			5.3.2.4 Multilabel classification
			5.3.2.5 Classification of multimodality imaging data
		5.3.3 Image segmentation with supervised machine learning
			5.3.3.1 Segmentation with convolutional neural networks
			5.3.3.2 Segmentation via statistical shape models
			5.3.3.3 Saliency-based segmentation
		5.3.4 Image synthesis with supervised machine learning
	5.4 Unsupervised learning
		5.4.1 Overview
		5.4.2 Unsupervised clustering
			5.4.2.1 Image segmentation via unsupervised clustering
		5.4.3 Unsupervised representation learning
			5.4.3.1 Statistical approaches for unsupervised representation learning
			5.4.3.2 Deep unsupervised representation learning
	5.5 Semisupervised learning
	5.6 Reinforcement learning
	5.7 Summary
	5.8 Questions
	References
SIX
. Health intelligence
	6.1 Introduction
	6.2 Predictive modeling and forecasting for health intelligence
	6.3 Multiple facets of health intelligence
		6.3.1 Global health intelligence
		6.3.2 Public and population health intelligence
			6.3.2.1 Social components of public and population health intelligence
			6.3.2.2 Population health intelligence and health disparities
			6.3.2.3 Ethical dilemmas in public and population health intelligence
		6.3.3 Personalized health and point-of-care intelligence
			6.3.3.1 Point-of-care analytics
			6.3.3.2 Research themes
				6.3.3.2.1 Heart rate characteristics
				6.3.3.2.2 Physiological multimodal methods
				6.3.3.2.3 Future directions
	6.4 Conclusions
	References
SEVEN
. Artificial intelligence in bioinformatics: automated methodology development for protein residue contact map prediction
	7.1 Background
	7.2 Evaluation of prediction performance
	7.3 Contact map prediction models
		7.3.1 Correlated mutation analysis
		7.3.2 Direct correlation analysis
			7.3.2.1 Direct-coupling analysis
			7.3.2.2 Sparse inverse covariance estimation
			7.3.2.3 Network deconvolution
		7.3.3 Supervised learning models
			7.3.3.1 Traditional machine learning models
			7.3.3.2 Convolutional neural network-based models
	7.4 Performance significantly depends on MSA features
	7.5 Conclusions
	References
EIGHT
. Deep learning in biomedical image analysis
	8.1 Introduction—deep learning meets medical image analysis
	8.2 Basics of deep learning
		8.2.1 Feed-forward neural networks
		8.2.2 Stacked autoencoder
		8.2.3 Convolutional neural networks
		8.2.4 Tips to reduce overfitting
		8.2.5 Open-source software toolkits for deep learning
		8.2.6 Brief summary of deep learning in biomedical imaging
	8.3 Applications in biomedical imaging
		8.3.1 Deep feature representation learning in the medical imaging area
		8.3.2 Medical image segmentation using deep learning
		8.3.3 Nuclear segmentation in mouse microscopy images using convolutional neural networks
			8.3.3.1 3-D convolutional neural network for cell segmentation
			8.3.3.2 Cascaded convolution neural network using contextual features
			8.3.3.3 Advantage of cascaded convolutional neural network over single convolutional neural network
			8.3.3.4 Evaluation of cell segmentation accuracy with comparison to current state-of-the-art methods
	8.4 Conclusion
	References
NINE
. Automatic lesion detection with three-dimensional convolutional neural networks
	9.1 Introduction
	9.2 3-D convolutional neural network
		9.2.1 3-D convolutional kernel
		9.2.2 3-D CNN hierarchical model
	9.3 Efficient fully convolutional architecture
		9.3.1 Fully convolutional transformation
		9.3.2 3-D score volume generation
		9.3.3 Score volume index mapping
	9.4 Two-stage cascaded framework for detection
		9.4.1 Candidate screening stage
		9.4.2 False positive reduction stage
	9.5 Case study I: cerebral microbleed detection in brain magnetic resonance imaging
		9.5.1 Background of the application
		9.5.2 Dataset, preprocessing and evaluation metrics
		9.5.3 Experimental results
	9.6 Case study II: lung nodule detection in chest computed tomography
		9.6.1 Background of the application
		9.6.2 Improved learning strategy
		9.6.3 Dataset, preprocessing and evaluation metrics
		9.6.4 Experimental results
	9.7 Discussion
	9.8 Conclusions
	Acknowledgments
	References
TEN
. Biomedical image segmentation for precision radiation oncology
	10.1 Introduction
	10.2 Graph models in biomedical image segmentation
		10.2.1 Graph nodes
		10.2.2 Graph edges
			10.2.2.1 Nodes connection
			10.2.2.2 Weighting function
		10.2.3 Graph matrices
		10.2.4 Graph-theoretic methods in target object segmentation
			10.2.4.1 Random walker–based models
			10.2.4.2 Graph Cut, Normalized Cut and Average Cut
		10.2.5 Applications in medical image segmentation
	10.3 Deep network in object detection and segmentation
		10.3.1 Deep object detection
			10.3.1.1 Region-based convolutional neural network–based models
			10.3.1.2 Multiscale location-aware kernel representation
		10.3.2 Deep image segmentation
			10.3.2.1 Architecture of mask region-based convolutional neural networks
	10.4 Applications for medical image processing
		10.4.1 Nucleus segmentation
		10.4.2 Ultrasound image segmentation
	10.5 Computational delineation and quantitative heterogeneity analysis for personalized radiation treatment planning
	10.6 Summary
	References
ELEVEN
. Content-based large-scale medical image retrieval
	11.1 Introduction
	11.2 Fundamentals of content-based image retrieval
		11.2.1 General framework architecture
		11.2.2 Image features used in retrieval
		11.2.3 Retrieval in medical imaging
	11.3 Visual feature-based retrieval
		11.3.1 Retrieval based on color
		11.3.2 Retrieval based on texture
	11.4 Geometric spatial feature-based retrieval
		11.4.1 Retrieval based on shape
		11.4.2 Retrieval by 3-D volumetric features
		11.4.3 Retrieval by spatial relationships
	11.5 Clinical contextual and semantic retrieval
		11.5.1 Retrieval by semantic pathology interpretation
		11.5.2 Retrieval based on generic models
		11.5.3 Retrieval based on physiological functional features
		11.5.4 Understanding visual features and their relationship to retrieved data
	11.6 Summary
	11.7 Exercises
	Acknowledgments
	References
TWELVE
. Diversity and novelty in biomedical information retrieval
	12.1 Introduction and motivation
	12.2 Overview of novelty and diversity boosting in biomedical information retrieval
	12.3 Boosting diversity and novelty in biomedical information retrieval
		12.3.1 Boosting novelty by maximal marginal relevance
		12.3.2 Boosting novelty by probabilistic latent semantic analysis
		12.3.3 Boosting diversity by relevance-novelty graphical model
	12.4 Diversity and novelty evaluation metrics
		12.4.1 Subtopic retrieval metrics
		12.4.2 α-nDCG
		12.4.3 geNov
	12.5 Evaluation results of diversity and novelty metrics
		12.5.1 Sensitiveness to the ranking qualities
		12.5.2 Discriminative power and running time
	12.6 Summary and future work
	Acknowledgments
	References
THIRTEEN
. Toward large-scale histopathological image analysis via deep learning
	13.1 Introduction
	13.2 Unique challenges in histopathological image analysis
	13.3 Computer-aided diagnosis for histopathological image analysis
		13.3.1 Fine-grained analysis of regions of interest
		13.3.2 High-level analysis of whole-slide images
		13.3.3 Deep learning acceleration for histopathological image analysis
	13.4 Deep learning for histopathological image analysis
		13.4.1 Overview
		13.4.2 Patch encoding with convolutional neural networks
		13.4.3 Accurate prediction via two-dimensional long short-term memory
		13.4.4 Loss function
		13.4.5 Results and discussions
	13.5 High-throughput histopathological image analysis
		13.5.1 Overview
		13.5.2 Small-capacity network
		13.5.3 Transfer learning from large-capacity network
		13.5.4 Feature adaptation from intermediate layers
		13.5.5 Efficient inference
			13.5.5.1 Results and analysis
	13.6 Summary
	References
FOURTEEN
. Data modeling and simulation
	14.1 Introduction
	14.2 Compartmental models
		14.2.1 Tracee model
		14.2.2 Tracer model
		14.2.3 Linking tracer and tracee models
	14.3 Model identification
		14.3.1 A priori identifiability
			14.3.1.1 Examples
			14.3.1.2 Definitions
			14.3.1.3 The model is a priori
			14.3.1.4 The transfer function method
		14.3.2 Parameter estimation
			14.3.2.1 Weighted least squares
			14.3.3.1 Residuals and weighted residuals defined for the aforementioned linear case
			14.3.3.2 Test of model order
	14.4 Model validation
		14.4.1 Simulation
	14.5 Case study
	14.6 Quantification of medical images
		14.6.1 Positron-emission tomography
		14.6.2 Blood flow
			14.6.2.1 Glucose metabolism
			14.6.2.2 Receptor binding
		14.6.3 Arterial spin labeling–magnetic resonance imaging
		14.6.4 Dynamic susceptibility contrast magnetic resonance imaging
	14.7 Exercises
	References
	Further reading
FIFTEEN
. Image-based biomedical data modeling and parametric imaging
	15.1 Introduction
		15.1.1 Anatomical and molecular imaging
		15.1.2 Compartmental models
		15.1.3 Kinetic modeling in molecular imaging
		15.1.4 Parameter estimation and parametric images in molecular imaging
		15.1.5 Compartment model parameter estimation
			15.1.5.1 Nonlinear least squares fitting
			15.1.5.2 Steady state techniques
	15.2 Parametric image estimation methods
		15.2.1 Autoradiographic technique
		15.2.2 Standardized uptake value method
		15.2.3 Integrated projection method
		15.2.4 Weighted integrated method
			15.2.5 Spectral analysis
		15.2.6 Graphical analysis methods
			15.2.6.1 Patlak graphical analysis
			15.2.6.2 Logan graphical analysis
			15.2.6.3 Yokoi plot
			15.2.6.4 Relative equilibrium-based graphical plot
		15.2.7 Linear least squares method
			15.2.7.1 Linear least squares
			15.2.7.2 Generalized linear least squares
			15.2.7.3 Improved versions for generalized linear least squares methods
			15.2.7.4 Multiple linear analysis for irreversible radiotracer
		15.2.8 Parametric image reconstruction method
	15.3 Noninvasive methods
		15.3.1 Image-derived input function
		15.3.2 Reference tissue model
		15.3.3 Population-based input function and cascaded modeling approaches
	15.4 Applications of parametric imaging and kinetic modeling
		15.4.1 Blood flow parametric images
		15.4.2 Oxygen-consumption parametric images
		15.4.3 Glucose metabolism parametric images
		15.4.4 Receptor-specific parametric images
		15.4.5 Recent applications of kinetic modeling in preclinical and clinical studies
	15.5 Summary
	References
SIXTEEN
. Molecular imaging in biology and pharmacology
	16.1 Introduction and background
		16.1.1 Basic elements and new developments in molecular imaging
		16.1.2 Recent developments in biology and pharmaceuticals
	16.2 Considerations for quantitative molecular imaging
		16.2.1 Input function
		16.2.2 Physiological/biological model
	16.3 Design/development of molecular imaging probes
		16.3.1 Chemical probes (small molecules)
		16.3.2 Biological probes (antibodies, peptides, aptamers)
	16.4 Molecular imaging of beta-amyloids and neurofibrillary tangles
		16.4.1 Brief review of molecular probes for beta-amyloid imaging
		16.4.2 In vitro characterization of FDDNP
		16.4.3 In vivo imaging of beta-amyloids and neurofibrillary tangles in Alzheimer disease
	16.5 Molecular imaging using antibody probes
		16.5.1 Imaging cell-surface phenotype
		16.5.2 Optimization of antibodies for in vivo targeting
		16.5.3 Measurement of target expression
		16.5.4 Monitoring response to therapy
	16.6 Some other molecular imaging applications
		16.6.1 In vivo regional substrate metabolism in human brain
		16.6.2 Cell proliferation rate in mouse tumor
		16.6.3 Measurement of murine cardiovascular physiology
	16.7 Summary and future perspectives
		16.7.1 Optical imaging, MicroSPECT, microfluidic blood sampler
		16.7.2 Automated image/data analysis
		16.7.3 Virtual experimentation
		16.7.4 Total-body imaging and tracer kinetics in the entire human body
		16.7.5 Artificial intelligence in molecular imaging
	16.8 Exercises
	References
SEVENTEEN
. Biomedical image visualization and display technologies
	17.1 Introduction
	17.2 Biomedical imaging modalities
		17.2.1 Single-modality volumetric biomedical imaging data
		17.2.2 Multimodality biomedical imaging
		17.2.3 Serial scans of biomedical imaging modalities
	17.3 Biomedical image visualization pipeline
	17.4 Volume rendering techniques
		17.4.1 Two-dimensional visualization
		17.4.2 Three-dimensional surface rendering visualization
		17.4.3 Three-dimensional direct volume rendering visualization
			17.4.3.1 Direct volume rendering computing pipeline
			17.4.3.2 Image semantic analysis for direct volume rendering visualization
			17.4.3.3 Transfer function designs
			17.4.3.4 Volume clipping and viewpoint selection
		17.4.4 Multimodality direct volume rendering visualization
		17.4.5 Direct volume rendering visualization for serial scans
	17.5 Display technology
		17.5.1 Two-dimensional conventional visualization display technologies
		17.5.2 Virtual reality visualization
		17.5.3 Augmented reality visualization
	17.6 Development platforms for biomedical image visualization
		17.6.1 Voreen (volume rendering engine)
		17.6.2 The visualization toolkit
		17.6.3 MeVisLab
	17.7 Conclusions
	17.8 Questions
	References
EIGHTEEN
. Biomedical image characterization and radiogenomics
	18.1 Introduction
	18.2 Radiomic characterization of medical imaging
		18.2.1 Handcrafted radiomic analysis
			18.2.1.1 Region of interest identification
				18.2.1.1.1 Tumor area.
				18.2.1.1.2 Heterogeneous intratumoral subregion.
					18.2.1.1.2.1 Tumor image heterogeneity evaluation by human definition.
					18.2.1.1.2.2 Tumor image heterogeneity evaluation by clustering analysis.
					18.2.1.1.2.3 Tumor heterogeneity analysis by image decomposition.
				18.2.1.1.3 Normal-appearing tissue area
					18.2.1.1.3.1 Background parenchyma that surrounds tumor.
					18.2.1.1.3.2 Tumor contralateral areas.
			18.2.1.2 Feature extraction and quantification
				18.2.1.2.1 Human-defined features.
				18.2.1.2.2 Semiautomatic approaches.
			18.2.1.3 Feature selection and predictive model building
		18.2.2 Deep learning-based radiomic analysis
		18.2.3 Multimodality/multiparametric radiomics
	18.3 Radiogenomics for uncovering cancer mechanism
		18.3.1 Individual genomic signatures
		18.3.2 Multiomics whole-genome genomic features
	18.4 Radiomics as signatures for non-invasive probes of cancer related molecular biomarkers
		18.4.1 Molecular subtypes prediction
		18.4.2 Clinical biomarkers
	18.5 Radiogenomic applications in cancer diagnosis and treatment
		18.5.1 Radiomics for tumor diagnosis
		18.5.2 Radiomic for prediction treatment response
		18.5.3 Radiomic for prediction of tumor prognosis
		18.5.4 Radiomic for prediction of tumor recurrence scores
		18.5.5 Integration of image and clinical/genomic features for cancer diagnosis and treatment
	18.6 Summary
	References
Part Three: Emerging technologies in biomedicine
NINETEEN
. Medical robotics and computer-integrated interventional medicine
	19.1 Introduction
	19.2 Technology and techniques
		19.2.1 System architecture
		19.2.2 Registration and transformations between coordinate systems
		19.2.3 Navigational trackers
		19.2.4 Robotic devices
		19.2.5 Intraoperative human–machine interfaces
		19.2.6 Sensorized instruments
		19.2.7 Software and robot control architectures
		19.2.8 Accuracy evaluation and validation
		19.2.9 Risk analysis and regulatory compliance
	19.3 Surgical CAD/CAM
		19.3.1 Example: robotically assisted joint reconstruction
		19.3.2 Example: needle placement
	19.4 Surgical assistance
		19.4.1 Basic concepts
		19.4.2 Surgical navigation systems as information assistants
		19.4.3 Surgeon extenders
		19.4.4 Auxiliary surgeon supports
		19.4.5 Remote telesurgery and telementoring
		19.4.6 Toward “intelligent” surgical assistance
	19.5 Summary and conclusion
	19.6 Exercises
	References
TWENTY
. Virtual and augmented reality in medicine
	20.1 Introduction
	20.2 Surgical education with virtual reality technologies
		20.2.1 Laparoscopic virtual reality surgery simulations
			20.2.1.1 Minimally invasive surgery trainer—virtual reality [31]
			20.2.1.2 LapSim [28]
			20.2.1.3 Laparoscopy virtual reality [26]
			20.2.1.4 SINERGIA [32]
		20.2.2 Arthroscopy training with virtual reality
	20.3 Minimally invasive surgery with augmented reality
		20.3.1 Neurosurgery with augmented reality
		20.3.2 Soft-tissue surgery with augmented reality
		20.3.3 Catheterized interventional procedures with augmented reality
		20.3.4 Orthopedic surgery
		20.3.5 Intravenous injection
	20.4 Mental health care with virtual reality and augmented reality technologies
		20.4.1 Virtual reality
		20.4.2 Augmented reality
	20.5 Other medical applications with virtual and augmented reality technologies
		20.5.1 Telementoring
		20.5.2 Anatomy education
	20.6 Future research and development opportunities as well as challenges in the healthcare zone
	20.7 Summary
	References
	Further reading
TWENTY ONE
. Sensory information feedback for neural prostheses
	21.1 Introduction
	21.2 Background: anatomy and physiology of the somatosensory system
		21.2.1 Somatosensory receptors
			21.2.1.1 Touch
			21.2.1.2 Proprioception
		21.2.2 Thermoreception and nociception
		21.2.3 Properties of somatosensory receptors
			21.2.3.1 Location
			21.2.3.2 Intensity
			21.2.3.3 Duration
		21.2.4 Integration of somatosensory input
		21.2.5 Spinal reflexes
		21.2.6 Ascending sensory pathways
		21.2.7 Dorsal column–medial lemniscus tract
			21.2.7.1 Spinothalamic tract
			21.2.7.2 Spinocerebellar tract
	21.3 Overview of sensory feedback in neural prostheses
	21.4 Anatomical targets and interface technologies for stimulating somatosensory inputs
		21.4.1 Transcutaneous targets and techniques
			21.4.1.1 Vibrotactile
			21.4.1.2 Electrotactile
			21.4.1.3 Applications
		21.4.2 Implantable targets and techniques
	21.5 Anatomical targets and interface technologies for sensing somatosensory inputs
	21.6 Summary and future directions
		21.6.1 Summary
		21.6.2 Future directions
			21.6.2.1 Technology
			21.6.2.2 Neural reinnervation (surgical)
	References
TWENTY TWO
. Mobile health (m-health): evidence-based progress or scientific retrogression
	22.1 Introduction
		22.1.1 What is mobile health?
		22.1.2 Defining mobile health and rapprochement with digital health
		22.1.3 Advances in the triangular pillars of mobile health
		22.1.4 The evidence of mobile health: market progress or clinical retrogression
		22.1.5 m-Health for diabetes care: an exemplar of market v/s clinical retrogression
	22.2 The science of mobile health: recent developments and challenges
	22.3 Conclusions
	References
	Further reading
TWENTY THREE
. Health and medical behavior informatics
	23.1 Introduction
	23.2 Behavior and behavior informatics
		23.2.1 Behavior
		23.2.2 Behavior informatics
			23.2.2.1 Behavior representation and reasoning
			23.2.2.2 Behavior analysis and learning
			23.2.2.3 Behavior management and applications
		23.2.3 Applications of behavior informatics
	23.3 Health and medical behavior
		23.3.1 Health behavior
		23.3.2 Medical behavior
	23.4 Health and medical behavior informatics
		23.4.1 Health behavior informatics
			23.4.1.1 Health behavior acquisition and construction
			23.4.1.2 Health behavior modeling and representation
			23.4.1.3 Health behavior analysis, learning and evaluation
			23.4.1.4 Health behavior management and applications
		23.4.2 Medical behavior informatics
			23.4.2.1 Medical behavior acquisition and construction
			23.4.2.2 Medical behavior modeling and representation
			23.4.2.3 Medical behavior analysis, learning, and evaluation
			23.4.2.4 Medical behavior applications and management
		23.4.3 Integrative health and medical behavior informatics
	23.5 Related work
		23.5.1 Connection to health behavior research
		23.5.2 Connection to behavioral medicine
		23.5.3 Connection to health/medical informatics and medical imaging
	23.6 Prospects
	Acknowledgment
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z
Back Cover