Artificial intelligence, machine learning, and deep learning in precision medicine in liver diseases : concept, technology, application and perspectives /
Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine and Liver Diseases: Concept, Technology, Application, and Perspectives combines four major applications of artificial intelligence (AI) within the field of clinical medicine specific to liver diseases: radiology imagi...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
London ; San Diego, CA :
Academic Press, an imprint of Elsevier,
[2023]
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 1
- Basics of artificial intelligence in medicine
- 1
- Artificial intelligence in health care: past and present
- Chapter outlines
- Clinical applications
- Introduction
- Past: a brief history of artificial intelligence in health care
- Present: artificial intelligence in health care today
- Image-based applications
- Electronic health record mining
- Reinforcement learning for identifying effective treatments
- Wearables
- Pandemic response
- Genomics
- Future: opportunities and challenges of artificial intelligence in health care
- Conclusion
- References
- 2
- Data-centric artificial intelligence in health care: progress, shortcomings, and remedies
- Introduction
- Training data generation and aggregation
- Data augmentation
- Federated learning
- Transfer representation learning
- Method specifications
- Empirical study
- Results of transfer representation learning for otitis media
- Results of transfer representation learning for otitis media
- Results of transfer representation learning for melanoma
- Results of transfer representation learning for melanoma
- Qualitative evaluation: visualization
- Qualitative evaluation: visualization
- Observations on transfer learning
- Observations on transfer learning
- Generative adversarial networks
- Method specifications
- Empirical study
- Experiment setup
- Experiment setup
- Experiment results
- Experiment results
- Fusing knowledge with generative adversarial networks
- Information from knowledge layers and structures.
- Information from knowledge graph and dictionary
- Method specifications
- Empirical study
- Experiment setup
- Experiment results
- Concluding remarks
- References
- 2
- Fields of artificial intelligence in hepatology, by tools,data preparation,methodology andapplication
- 3
- Artificial intelligence in radiology and its application in liver disease
- Chapter outlines
- Clinical applications
- Introduction
- Radiomics
- Radiomics workflow
- Pre-processing
- Segmentation
- Radiomics feature extraction
- Feature selection and model building
- Clinical application of radiomics in liver imaging
- Chronic liver disease
- Classification of focal liver lesions
- Prognostication of hepatic malignancy
- Limitations and future perspectives of radiomics
- Deep learning
- Development and validation of deep learning algorithm
- Application of deep learning in liver imaging
- Liver and abdominal organ segmentation
- Quantification and classification of diffuse liver abnormalities
- Detection, segmentation, and classification of liver tumors
- Application of deep learning-based body composition analysis in liver disease
- Image quality improvement
- Limitations and future perspectives of deep learning
- Conclusion
- References
- 4
- Electronic health record for artificial intelligence health care, and application to liver disease
- Chapter outlines
- Clinical applications
- Introduction
- Electronic health records in precision health
- Precision medicine and precision health
- Earlier medicine
- Electronic health records applied to prediction of fatty liver
- Data preprocessing
- Variable selection
- Validation
- Time series data used to predict liver cancer risk
- Preprocessing: electronic health record images
- Convolutional neural network model building
- Validation.
- Natural language processing for time-series coded data
- Conclusion
- References
- 5
- Artificial intelligence in pathology and application to liver disease
- Chapter outlines
- Clinical applications
- Introduction
- Role of pathology in liver disease diagnosis and staging
- Digital revolution of pathology
- Principles of artificial intelligence processing of whole-slide images
- Applications of artificial intelligence-based pathology
- Automated or assisted diagnosis
- Artificial intelligence-based prognostication
- Artificial intelligence-based pathology and the next generation of biomarkers
- Challenges to implementing artificial intelligence in pathology
- Conclusion
- References
- 6
- Artificial intelligence using multiomics/genetic tools and application in liver disease
- Introduction
- Multiomics data and their integration in hepatocellular carcinoma
- Cardinal data resources for multiomics analysis
- Applications of high-throughput multiomics hepatocellular carcinoma data
- Subtype and subgroup identification
- Diagnostic markers
- Prognostic markers
- Therapeutic markers
- Conclusion
- Clinical applications
- References
- 3
- Artificial intelligenc eapplication inspecific diseasesof liver
- 7
- Artificial intelligence in prediction of steatosis and fibrosis of nonalcoholic fatty liver disease
- Chapter outlines
- Clinical applications
- Introduction
- Current methods for assessing steatosis
- Artificial intelligence for predicting steatosis
- Current methods for assessing liver fibrosis
- Artificial intelligence for assessing histologic fibrosis
- Conclusions and the future
- References
- 8
- Artificial intelligence in the prediction of progression and outcomes in viral hepatitis
- Chapter outlines
- Clinical applications
- A brief introduction to artificial intelligence.
- Artificial intelligence in the detection or prediction of liver fibrosis in chronic viral hepatitis
- Artificial intelligence in predicting gastroesophageal varices using computed tomography images
- Artificial intelligence in the diagnosis, prediction, and prognosis of hepatocellular carcinoma
- Artificial intelligence in predicting hepatocellular carcinoma occurrence
- Artificial intelligence in predicting survival of hepatocellular carcinoma based on multiomics data
- Artificial intelligence for clinical outcome prediction using histopathology images
- Artificial intelligence in identifying microvascular invasion for predicting clinical outcomes of hepatocellular carcinoma
- Future perspectives and limitations of artificial intelligence technology
- Conclusion
- References
- 9
- Artificial intelligence in cirrhosis complications and acute liver failure
- Chapter outlines
- Definition of terms
- Clinical applications
- Introduction
- Portal hypertension
- Gastroesophageal varices
- Ascites
- Hepatic encephalopathy
- Hepatorenal syndrome
- Portal vein thrombosis
- Transplantation and hepatocellular carcinoma
- Acute-on-chronic liver failure
- Acute liver failure
- Challenges
- References
- 10
- Artificial intelligence in liver transplantation
- Chapter outline
- Clinical applications
- Introduction
- Pretransplant
- Waiting list mortality
- Organ allocation
- Donor organ assessment
- Donor-recipient matching
- Summary
- Posttransplant
- Patient survival
- Prediction of graft rejection and failure
- Other post-transplant complications
- Recurrent hepatocellular carcinoma
- Metabolic disease
- Acute kidney injury
- Summary
- Future directions
- Conclusion
- References
- 11
- Artificial intelligence in liver cancer: diagnosis and management
- Chapter outlines
- Clinical applications
- Introduction.
- Overview of main machine learning models used in field of hepatocellular carcinoma
- Artificial intelligence-based differential diagnosis of hepatocellular carcinoma
- Hepatocellular carcinoma diagnosis by artificial intelligence based on multiple biomarkers
- Differential diagnosis based on the findings of ultrasonography
- Differential diagnosis based on the findings of computed tomography
- Differential diagnosis based on findings of magnetic resonance imaging
- Artificial intelligence-based prediction of treatment response of hepatocellular carcinoma
- Artificial intelligence-based prediction of prognosis of hepatocellular carcinoma
- Conclusion
- References
- 12
- Predicting drug-induced liver injury with artificial intelligence-a minireview
- Disclaimer
- Chapter outlines
- Clinical applications
- What is drug-induced liver injury?
- Why drug-induced liver injury is important to drug development and public health
- Nonanimal approaches developed for drug-induced liver injury assessment
- How the drug-induced liver injury risk of a drug is determined
- Overview of computational methods for drug-induced liver injury prediction
- Discussion
- Conclusion
- Author contributions
- Conflict of interest
- References
- 13
- Artificial intelligence in precision medicine and liver disease monitoring
- Chapter outlines
- Clinical applications
- Precision medicine
- Four key steps to achieving precision medicine
- Data from multiple sources
- Methodology for data generation
- Artificial intelligence: tool to achieve precision medicine
- Applications: All of Us as an example
- From precision medicine to precision health and precision public health
- Artificial intelligence in monitoring liver disease
- Digital medicine
- Mobile health
- Digital tracking system
- Smart mirrors
- Applications.