Artificial Intelligence in Medicine: Technical Basis and Clinical Applications

Artificial Intelligence in Medicine
Copyright
Dedication
Contents
List of contributors
Foreword
References
Preface
Acknowledgments
1 Artificial intelligence in medicine: past, present, and future
1.1 Introduction
1.2 A brief history of artificial intelligence and its applications in medicine
1.3 How intelligent is artificial intelligence?
1.4 Artificial intelligence, machine learning, and precision medicine
1.5 Algorithms and models
1.6 Health data sources and types
1.7 The promise
1.8 The challenges
1.8.1 Quality and completeness of training data
1.8.2 Trust and performance: the case for model interpretability
1.8.3 Beyond performance and interpretability: causality
1.8.4 Defining the question, measuring real-world impact
1.8.5 Maximizing information gain across modalities, tasks, populations, and time
1.8.6 Quality assessment and expert supervision
1.9 Making it a reality: integrating artificial intelligence into the human workforce of a learning health system
References
2 Artificial intelligence in medicine: Technical basis and clinical applications
2.1 Introduction
2.2 Technology used in clinical artificial intelligence tools
2.2.1 Elements of artificial intelligence algorithms
2.2.1.1 Activation functions
2.2.1.2 Fully connected layer
2.2.1.3 Dropout
2.2.1.4 Residual blocks
2.2.1.5 Initialization
2.2.1.6 Convolution and transposed convolution
2.2.1.7 Inception layers
2.2.2 Popular artificial intelligence software architectures
2.2.2.1 Neural networks and fully connected networks
2.2.2.2 Convolutional neural networks
2.2.2.3 U-Nets and V-Nets
2.2.2.4 DenseNets
2.2.2.5 Generative adversarial networks
2.2.2.6 Hybrid generative adversarial network designs
2.3 Clinical applications
2.3.1 Applications of regression
2.3.1.1 Bone age
2.3.1.2 Brain age
2.3.2 Applications of segmentation
2.3.3 Applications of classification
2.3.3.1 Detection of disease
2.3.3.2 Diagnosis of disease class
2.3.3.3 Prediction of molecular markers
2.3.3.4 Prediction of outcome and survival
2.3.4 Deep learning for improved image reconstruction
2.4 Future directions
2.4.1 Understanding what artificial intelligence “sees”
2.4.2 Workflow
2.5 Conclusion
References
3 Deep learning for biomedical videos: perspective and recommendations
3.1 Introduction
3.2 Video datasets
3.3 Semantic segmentation
3.4 Object detection and tracking
3.5 Motion classification
3.6 Future directions and conclusion
References
4 Biomedical imaging and analysis through deep learning
4.1 Introduction
4.2 Tomographic image reconstruction
4.2.1 Foundation
4.2.2 Computed tomography
4.2.3 Magnetic resonance imaging
4.2.4 Other imaging modalities
4.3 Image segmentation
4.3.1 Introduction
4.3.2 Localization versus segmentation
4.3.3 Fully convolutional networks
4.3.4 Regions with convolutional neural network features
4.3.5 A priori information
4.3.6 Manual labeling
4.3.7 Semisupervised and unsupervised approaches
4.4 Image registration
4.4.1 Single-modality image registration
4.4.2 Multimodality image registration
4.5 Deep-learning-based radiomics
4.5.1 Detection
4.5.2 Characterization and diagnosis
4.5.3 Prognosis
4.5.4 Assessment and prediction of response to treatment
4.5.5 Assessment of risk of future cancer
4.6 Summary and outlook
References
5 Expert systems in medicine
5.1 Introduction
5.2 A brief history
5.3 Methods
5.3.1 Expert system architecture
5.3.2 Knowledge representation and management
5.3.3 Uncertainty, probabilistic reasoning, fuzzy logic
5.3.3.1 Uncertainty
5.3.3.2 Probabilistic reasoning
5.3.3.3 Fuzzy logic
5.4 Applications
5.4.1 Computer-assisted diagnosis
5.4.2 Computer-assisted therapy
5.4.3 Medication alert systems
5.4.4 Reminder systems
5.5 Challenges
5.5.1 Workflow integration
5.5.2 Clinician acceptance and alert fatigue
5.5.3 Knowledge maintenance
5.5.4 Standard, transferability, and interoperability
5.6 Future directions
References
6 Privacy-preserving collaborative deep learning methods for multiinstitutional training without sharing patient data
6.1 Introduction
6.2 Variants of distributed learning
6.2.1 Model ensembling
6.2.2 Cyclical weight transfer
6.2.3 Federated learning
6.2.4 Split learning
6.3 Handling data heterogeneity
6.4 Protecting patient privacy
6.5 Publicly available software
6.6 Conclusion
References
7 Analytics methods and tools for integration of biomedical data in medicine
7.1 The rise of multimodal data in biology and medicine
7.1.1 The emergence of various sequencing techniques
7.1.1.1 Bulk sequencing
7.1.1.2 Single-cell sequencing
7.1.2 The increasing need for combining images and omics in clinical applications
7.1.2.1 Various modalities of images in clinics
7.1.2.2 The rise of radiomics: combine medical images with omics
7.1.3 The availability of large-scale public health data
7.2 The challenges in multimodal data—problems with learning from multiple sources of data
7.2.1 The imperfect generation of single-cell data
7.2.1.1 The complementariness of various sources of data
7.2.2 The issues of generalizability of machine learning
7.3 Machine learning algorithms in integrating medical and biological data
7.3.1 Genome-wide data integration with machine learning
7.3.1.1 How to integrate various omics for cancer subtyping
7.3.1.2 How to integrate single-cell multiomics for precision medicine
7.3.2 Data integration beyond omics—an example with cardiovascular diseases
7.3.2.1 How to integrate various image modalities such as magnetic resonance imaging computed tomography scans
7.3.2.2 How to better the diagnosis by linking images with electrocardiograms
7.3.3 Multimodal decision-making in clinical settings
7.4 Future directions
References
8 Electronic health record data mining for artificial intelligence healthcare
8.1 Introduction
8.2 Overview of the electronic health record
8.2.1 History of the electronic health record
8.2.2 Core functions of an electronic health record
8.2.3 Electronic health record ontologies and data standards
8.3 Clinical decision support
8.3.1 Healthcare primed for clinical decision support
8.4 Areas of artificial intelligence augmentation for electronic health records
8.4.1 Artificial intelligence to improve data entry and extraction
8.4.2 Optimizing care
8.4.3 Predictions
8.4.4 Hospital outcomes
8.4.5 Sepsis and infections
8.4.6 Oncology
8.5 Limitations of artificial intelligence and next steps
References
9 Roles of artificial intelligence in wellness, healthy living, and healthy status sensing
9.1 Introduction
9.2 Diet
9.3 Fitness and physical activity
9.4 Sleep
9.5 Sexual and reproductive health
9.6 Mental health
9.7 Behavioral factors
9.8 Environmental and social determinants of health
9.9 Remote screening tools
9.10 Conclusion
References
10 The growing significance of smartphone apps in data-driven clinical decision-making: Challenges and pitfalls
10.1 Introduction
10.2 Distribution of apps in the field of medicine
10.3 Distribution of apps over different locations
10.4 Reporting applications development approaches
10.5 Decision-support modalities
10.6 Camera-based apps
10.7 Guideline/algorithm applications
10.8 Predictive modeling applications
10.9 Sensor-linked apps
10.10 Discussion
10.11 Summary
References
11 Artificial intelligence for pathology
11.1 Introduction
11.2 Deep neural networks
11.2.1 Convolutional neural networks
11.2.2 Fully convolutional networks
11.2.3 Generative adversarial networks
11.2.4 Stacked autoencoders
11.2.5 Recurrent neural networks
11.3 Deep learning in pathological image analysis
11.3.1 Image classification
11.3.1.1 Image-level classification
11.3.1.2 Object-level classification
11.3.2 Object detection
11.3.2.1 Detection of particular types of objects
11.3.2.2 Detection of objects without category labeling
11.3.2.3 Detection of objects with category labeling
11.3.3 Image segmentation
11.3.3.1 Nucleus/cell segmentation
11.3.3.2 Gland segmentation
11.3.3.3 Segmentation of other biological structures or tissues
11.3.4 Stain normalization
11.3.5 Image superresolution
11.3.6 Computer-aided diagnosis
11.3.7 Others
11.4 Summary
11.4.1 Open challenges and future directions of deep learning in pathology image analysis
11.4.1.1 Quality control
11.4.1.2 High image dimension
11.4.1.3 Object crowding
11.4.1.4 Data annotation issues
11.4.1.5 Integration of different types of input data
11.4.2 Outlook of clinical adoption of artificial intelligence
11.4.2.1 Potential applications
11.4.2.2 Barriers to clinical adoption
11.4.2.2.1 Lagging adoption of digital pathology
11.4.2.2.2 Lack of standards for interfacing AI to clinical systems
11.4.2.2.3 Regulatory concerns
11.4.2.2.4 Computational requirements
11.4.2.2.5 Algorithm explainability
11.4.2.2.6 Pathologists’ skepticism
References
12 The potential of deep learning for gastrointestinal endoscopy—a disruptive new technology
12.1 Introduction
12.2 Applications of artificial intelligence in video capsule endoscopy
12.2.1 Introduction
12.2.2 Decreasing read time
12.2.3 Anatomical landmark identification
12.2.4 Improving sensitivity
12.2.5 Recent developments
12.3 Applications of artificial intelligence in upper endoscopy
12.3.1 Introduction
12.3.2 Esophageal cancer
12.3.3 Gastric cancer
12.3.4 Upper endoscopy quality
12.3.5 Future directions
12.4 Applications of artificial intelligence in colonoscopy
12.4.1 Introduction
12.4.2 Cecal intubation rate and cecal intubation time
12.4.3 Withdrawal time
12.4.4 Boston Bowel Prep Scoring
12.4.5 Polyp detection
12.4.6 Polyp size
12.4.7 Polyp morphology
12.4.8 Polyp pathology
12.4.9 Tools
12.4.10 Mayo endoscopic subscore
12.5 Conclusion
12.6 Future directions
References
13 Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic r...
13.1 Introduction
13.2 Historical artificial intelligence for diabetic retinopathy
13.3 Deep learning era
13.4 Lessons from interpreting and evaluating studies
13.5 Important factors for real-world usage
13.6 Regulatory approvals and further validation
13.7 Toward patient impact and beyond
13.8 Summary
Conflict of interest
References
14 Artificial intelligence in radiology
14.1 Introduction
14.2 Thoracic applications
14.2.1 Pulmonary analysis in chest X-ray
14.2.2 Pulmonary analysis in computerized tomography
14.2.2.1 Lung, lobe, and airway segmentation
14.2.2.2 Interstitial lung disease pattern recognition
14.3 Abdominal applications
14.3.1 Pancreatic cancer analysis in computerized tomography and magnetic resonance imaging
14.3.1.1 Pancreas segmentation in computerized tomography and magnetic resonance imaging
14.3.1.2 Pancreatic tumor segmentation and detection in computerized tomography and magnetic resonance imaging
14.3.1.3 Prediction and prognosis with pancreatic cancer imaging
14.3.2 AI in other abdominal imaging
14.4 Pelvic applications
14.5 Universal lesion analysis
14.5.1 DeepLesion dataset
14.5.2 Lesion detection and classification
14.5.3 Lesion segmentation and quantification
14.5.4 Lesion retrieval and mining
14.6 Conclusion
References
15 Artificial intelligence and interpretations in breast cancer imaging
15.1 Introduction
15.2 Artificial intelligence in decision support
15.3 Artificial intelligence in breast cancer screening
15.4 Artificial intelligence in breast cancer risk assessment: density and parenchymal pattern
15.5 Artificial intelligence in breast cancer diagnosis and prognosis
15.6 Artificial intelligence for treatment response, risk of recurrence, and cancer discovery
15.7 Conclusion and discussion
References
16 Prospect and adversity of artificial intelligence in urology
16.1 Introduction
16.2 Basic examinations in urology
16.2.1 Urinalysis and urine cytology
16.2.2 Ultrasound examination
16.3 Urological endoscopy
16.3.1 Cystoscopy and transurethral resection of the bladder
16.3.2 Ureterorenoscopy
16.4 Andrology
16.5 Diagnostic imaging
16.5.1 Prostate
16.5.2 Kidney
16.5.3 Ureter and bladder
16.6 Robotic surgery
16.6.1 Preoperative preparation
16.6.2 Navigation
16.6.3 Automated maneuver
16.7 Risk prediction
16.8 Future direction
References
17 Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imag...
17.1 Introduction
17.1.1 Workflow
17.1.1.1 Data acquisition
17.1.1.2 Preprocessing
17.1.1.3 Model building and evaluation
17.1.1.4 Inference
17.1.2 Meaningful incorporation of machine learning
17.2 Quantitative imaging
17.2.1 Brief overview of the physics of imaging modalities
17.2.2 Use of artificial intelligence in different stages of a quantitative imaging workflow
17.3 Risk assessment in cancer
17.4 Therapeutic outcome prediction
17.4.1 Chemotherapy
17.4.2 Radiation therapy
17.5 Using artificial intelligence meaningfully
17.6 Summary
References
18 Artificial intelligence in oncology
Abbreviations
18.1 Introduction
18.2 Electronic health records and clinical data warehouse
18.2.1 Data reuse for research purposes
18.2.2 Data reuse and artificial intelligence
18.2.3 Data reuse for patient care
18.3 Artificial intelligence applications for imaging in oncology
18.3.1 Applications in oncology for diagnosis and prediction
18.3.1.1 Computer vision and image analysis
18.3.1.2 Radiomics: data-driven biomarker discovery
18.3.1.3 Artificial intelligence–assisted diagnosis and monitoring in oncology
18.3.1.4 Treatment outcome assessment and prediction
18.3.2 Applications in oncology to improve exam quality and workflow
18.3.2.1 Improvement of image acquisition
18.3.2.2 Image segmentation
18.3.2.3 Improved workflow
18.3.2.4 Interventional radiology
18.4 Artificial intelligence applications for radiation oncology
18.4.1 Treatment planning
18.4.1.1 Segmentation
18.4.1.1.1 Brain
18.4.1.1.2 Head and neck
18.4.1.1.3 Lung
18.4.1.1.4 Abdomen
18.4.1.1.5 Pelvis
18.4.1.2 Dosimetry
18.4.2 Outcome prediction
18.4.2.1 Treatment response
18.4.2.1.1 Brain
18.4.2.1.2 Head and neck
18.4.2.1.3 Lung
18.4.2.1.4 Esophagus
18.4.2.1.5 Rectum
18.4.2.2 Toxicity
18.5 Future directions
References
19 Artificial intelligence in cardiovascular imaging
19.1 Introduction
19.2 Types of machine learning
19.3 Deep learning
19.4 Role of artificial intelligence in echocardiography
19.5 Role of artificial intelligence computed tomography
19.6 Role of artificial intelligence in nuclear cardiology
19.7 Role of artificial intelligence in cardiac magnetic resonance imaging
19.8 Role of artificial intelligence in electrocardiogram
19.9 The role of artificial intelligence in large databases
19.10 Our views on machine learning
19.11 Conclusion
References
20 Artificial intelligence as applied to clinical neurological conditions
20.1 Introduction to artificial intelligence in neurology
20.2 Integration with clinical workflow
20.2.1 Diagnosis
20.2.2 Risk prognostication
20.2.3 Surgical planning
20.2.4 Intraoperative guidance and enhancement
20.2.5 Neurophysiological monitoring
20.2.6 Clinical decision support
20.2.7 Theoretical neurological artificial intelligence research
20.3 Currently adopted methods in clinical use
20.4 Challenges
20.4.1 Data volume
20.4.2 Data quality
20.4.3 Generalizability
20.4.4 Interpretability
20.4.5 Legal
20.4.6 Ethical
20.5 Conclusion
References
21 Harnessing the potential of artificial neural networks for pediatric patient management
21.1 Introduction
21.2 Applications of artificial intelligence in diagnosis and prognosis
21.2.1 Prematurity
21.2.2 Childhood brain tumors
21.2.3 Epilepsy and seizure disorders
21.2.4 Autism spectrum disorder
21.2.5 Mood disorders and psychoses
21.2.6 Hydrocephalus
21.2.7 Traumatic brain injury
21.2.8 Molecular mechanisms of disease
21.2.9 Other disease entities
21.3 Transition to treatment decision-making using artificial intelligence
21.4 Future directions
References
22 Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control
22.1 Introduction
22.2 Artificial intelligence–enhanced data analysis for outbreak detection and early warning
22.2.1 Analyzing data collected from the physical world
22.2.2 Analyzing data from the cyberspace
22.2.3 From syndromic to pre-syndromic disease surveillance: A safety net for public health
22.3 Artificial intelligence–enhanced prediction in support of public health surveillance
22.3.1 Time series prediction based on dependent variables
22.3.2 Time series prediction based on dependent and independent variables
22.4 Artificial intelligence–based infectious disease transmission modeling and response assessment
22.4.1 Modeling disease transmission dynamics based on machine learning and complex networks
22.4.2 Modeling disease transmission dynamics based on multiagent modeling
22.5 Internet-based surveillance systems for global epidemic monitoring
22.6 Conclusion
References
23 Regulatory, social, ethical, and legal issues of artificial intelligence in medicine
23.1 Introduction
23.2 Ethical issues in data acquisition
23.2.1 Ethical issues arising from each type of data source
23.2.1.1 Ethical issues common to all data sources: Privacy and confidentiality
23.2.1.2 Ethical issues unique to each data source: Issues of consent
23.2.1.2.1 Issues of consent with data from research repositories
23.2.1.2.2 Return of results from research repositories
23.2.1.2.3 Issues of consent with clinical or public health data
23.2.1.2.4 Incidental or secondary findings in clinical or public health data
23.2.1.2.5 Issues of consent with nonclinically collected data
23.2.2 Future directions: Toward a new model of data stewardship
23.3 Application problems: Problems with learning from the data
23.3.1 Values embedded in algorithm design
23.3.2 Biases in the data themselves
23.3.3 Biases in the society in which the data occurs
23.3.4 Issues of implementation
23.3.5 Summary
23.4 Issues in regulation
23.4.1 Challenges to existing regulatory frameworks
23.4.2 Challenges in oversight and regulation of artificial intelligence used in healthcare
23.4.3 Regulation of safety and efficacy
23.4.4 Privacy and data protection
23.4.5 Transparency, liability, responsibility, and trust
23.5 Implications for the ethos of medicine
23.6 Future directions
References
24 Industry perspectives and commercial opportunities of artificial intelligence in medicine
24.1 Introduction
24.2 Exciting growth of artificial intelligence in medicine
24.3 A framework on development of artificial intelligence in medicine
24.3.1 The power of public attention and funding
24.3.2 Technology relies on continuous innovation
24.3.3 Practical applications bring the innovation to the real world
24.3.4 Market adoption defines the success
24.3.5 Apply the framework to the current and future market
24.3.6 Patient privacy
24.3.7 Approving a moving target
24.3.8 Accountability and transparency
24.4 Business opportunity of artificial intelligence in medicine
References
25 Outlook of the future landscape of artificial intelligence in medicine and new challenges
25.1 Overview of artificial intelligence in health care
25.1.1 Models dealing with input and output data from the same domain
25.1.2 Deep learning as applied to problems with input and output related by physical/mathematical law
25.1.3 Models with input and output data domains related by empirical evidence or measurements
25.1.4 Applications beyond traditional indications
25.2 Challenges ahead and issues relevant to the practical implementation of artificial intelligence in medicine
25.2.1 Technical challenges
25.2.2 Data, data curation, and sharing
25.2.3 Data and potential bias in artificial intelligence
25.2.4 Workflow and practical implementation
25.2.5 Clinical tests
25.2.6 Economical, political, social, ethical, and legal aspects
25.2.7 Education and training
25.3 Future directions and opportunities
25.4 Summary and outlook
References
Index