期刊
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
卷 9, 期 2, 页码 -出版社
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/tvst.9.2.11
关键词
optical coherence tomography; artificial intelligence; deep learning
资金
- NEI, Bethesda, MD [K23EY02492, K23EY029246]
- Research to Prevent Blindness, Inc. New York, NY
- Lowy Medical Research Institute
Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generalizes across all patient populations and OCT devices challenging. Several recent studies have reported high diagnostic performances of AI models; however, significant methodological challenges still exist in applying these models in real-world clinical practice. Lack of large-image datasets from multiple OCT devices, nonstandardized imaging or post-processing protocols between devices, limited graphics processing unit capabilities for exploiting 3-dimensional features, and inconsistency in the reporting metrics are major hurdles in enabling Al for OCT analyses. We discuss these issues and present possible solutions.
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