Ophthalmology is a proving ground for emerging machine-learning paradigms in medicine. The efforts of the ophthalmic and computer science communities have yielded thousands of innovative models, exploring tasks ranging from basic science to clinical epidemiology.

The Open Ophthalmology Database was developed in 2023 as the Vision Intelligence Network to provide a systematic means of identifying and implementing machine-learning models in ophthalmology. It leverages natural language processing to survey the published literature for pertinent models and characterize them in terms of their purpose, data substrate, and algorithmic approaches.

Key data cataloged in the database include the following:

Field Description
Model name of the model, defaulting to the primary author if model name is undetermined
Subspecialty ophthalmic subspecialty to which the model is most relevant
Title title of the corresponding publication
Task primary function of the model
Task type class of objective toward which the model is trained
Algorithm type primary class of algorithm used in the model
Data type primary data type used in training
Dataset size number of training examples
Paper link link to corresponding publication
Code link to provided codebase
Date publication date
Code identified whether or not a corresponding codebase was identified


Curation relies on hard-coded metadata extraction where possible. Where this is not feasible, the model invokes agentic tools to summarize pertinent data. While we make efforts to check the reliability of these outputs, we note that such tools are fundamentally probabilistic and may generate inaccurate summaries. All entries are linked to their respective publications, and users are advised to refer to the reference for all technical details regarding the model. This database is an experimental research effort and should not be used for clinical decision-making.

Inquiries are welcome at contact@openophthodb.org.