Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review

Figures & data

Figure 1. Flow diagram of open OCT data collection.

Table 1. Open OCT datasets details.

Name	Link	Device	Images	Diseases
Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification	https://data.mendeley.com/datasets/rscbjbr9sj/3	Spectralis	109309	Choroidal neovascularization, diabetic macular edema, drusen, normal
Duke Farsiu	https://people.duke.edu/~sf59/RPEDC_Ophth_2013_dataset.htm	Bioptigen OCT	38400	Intermediate AMD
Duke Fang	https://people.duke.edu/~sf59/Fang_TMI_2013.htm	Spectralis	763	AMD
Duke Chiu	http://www.duke.edu/~sf59/Chiu_BOE_2014_dataset.htm	Spectralis	610	DME
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning	https://datadryad.org/stash/dataset/doi:10.5061/dryad.2rbnzs7m4	Topcon 3D OCT-2000	2966	AMD
Data on OCT and Fundus Images.	https://data.mendeley.com/datasets/2rnnz5nz74/2	Topcon 3D OCT-1000	50	Glaucomatous
IACL - HCMS John Hopkins	https://iacl.ece.jhu.edu/index.php?title=Main_Page	Spectralis	1745	Multiple sclerosis
OCTID	https://borealisdata.ca/dataverse/OCTID	Cirrus HD OCT	470	Macular hole, AMD, diabetic retinopathy

Figure 2. Comparative Samples of OCT Imaging Technologies: A. Cirrus HD OCT from OCTID, B. Topcon 3D OCT-1000 from data on OCT and fundus images, C. Spectralis OCT by Heidelberg Engineering from labeled OCT and chest X-Ray images.

Figure 3. Distribution of patients in the OCT datasets per country. Map created adapting naturalearth_lowres’s layer using (c) 2013–2022, GeoPandas developers, an open-source python package created under the liberal terms of the BSD-3-Clause license.²¹.

Figure 4. Key components of the ideal OCT dataset.

GeoPandas. [cited 2 Sep 2023]. Available: https://geopandas.org/en/stable/about/citing.html

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