Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview

Abstract

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.

Keywords: OCT; data; data analysis; data sharing; datasets; open data; optical coherence tomography.

Figure 1

The dataset search with detailed search engines.

Figure 2

Exemplary sample images from datasets. (A)—Kermany et al.; (B)—Data of rare retinal disease; (C)—OCTID; (D)—Labeled Retinal Optical Coherence Tomography Dataset for Classification; (E)—OCT MS and HC data; (F)—Duke OCT.

Figure 3

Disease representation across datasets.

Figure 4

Origin of datasets.

Figure 5

Histogram for Kermany dataset.

Figure 6

Histogram for OCTID dataset.

Figure 7

Histogram for Labeled Retinal OCT Dataset for Classification dataset.