Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review
Name
Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm A Review.pdf
Description
Published version
Size
4.76 MB
Format
Adobe PDF
Checksum (MD5)
88f536d3af658114f15c5f5408c77953
Author(s)
Restrepo, David
Quion, Justin Michael
Do Carmo Novaes, Frederico
Azevedo Costa, Iago Diogenes
Vasquez, Constanza
Bautista, Alyssa Nicole
Quiminiano, Ellaine
Lim, Patricia Abigail
Mwavu, Roger
Celi, Leo Anthony
Date Issued
April 2, 2024
Journal
Seminars in Ophthalmology
Publisher
Taylor & Francis
Citation
Restrepo, D., Quion, J. M., Do Carmo Novaes, F., Azevedo Costa, I. D., Vasquez, C., Bautista, A. N., … Nakayama, L. F. (2024). Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review. Seminars in Ophthalmology, 39(3), 193–200.
Version
Final published version
Abstract
BACKGROUND: Imaging plays a pivotal role in eye assessment. With the introduction of advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging datasets in ophthalmology. While disparities and health inequalities hidden within data are well-documented, the ophthalmology field faces specific challenges to the creation and maintenance of datasets. Optical Coherence Tomography (OCT) is useful for the diagnosis and monitoring of retinal pathologies, making it valuable for AI applications. This review aims to identify and compare the landscape of publicly available optical coherence tomography databases for AI applications.
METHODS: We conducted a literature review on OCT and AI articles with publicly accessible datasets, using PubMed, Scopus, and Web of Science databases. The review retrieved 183 articles, and after full-text analysis, 50 articles were included. From the included articles were identified 8 publicly available OCT datasets, focusing on patient demographics and clinical details for thorough assessment and comparison.
RESULTS: The resulting datasets encompass 154,313 images collected from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices. These datasets included normal exams, age-related macular degeneration, and diabetic maculopathy, among others. Comprehensive demographic information is available in one dataset and the USA is the most represented population.
DISCUSSION: Current publicly available OCT databases for AI applications exhibit limitations, stemming from their non-representative nature and the lack of comprehensive demographic information. Limited datasets hamper research and equitable AI development. To promote equitable AI algorithmic development in ophthalmology, there is a need for the creation and dissemination of more representative datasets.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
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DOI of Published Version
https://doi.org/10.1080/08820538.2024.2308248
