Work

Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review

Year: 2020

Type: article

Abstract: 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 AI for OCT analyses. We discuss these issues and present possible solutions.

Source: Translational Vision Science & Technology

DOI: 10.1167/tvst.9.2.11

Authors: Ryan T. Yanagihara, Cecilia S. Lee, Daniel Shu Wei Ting, Aaron Y. Lee

Institutions: University of Washington, National University of Singapore, Singapore National Eye Center, Duke-NUS Medical School

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,850