Researchers present RETFound, a self-supervised learning masked autoencoder-based foundation model for retinal images.
By Pooja Toshniwal PahariaSep 18 2023Reviewed by Benedette Cuffari, M.Sc. Artificial intelligence models have been validated for recognizing signs of eye diseases in retinal images, enhancing diagnosis, and risk stratification. Combining natural images and medical data, these models provide reliable disease prediction, thereby enabling efficient risk stratification in fields like chest X-rays and dermatology imaging.
About the study RETFound is a SSL model that was trained on 1.6 million unlabeled retinal images. An improved SSL-based approach was used on natural images and retinal images retrieved from the Moorfields diabetic image dataset with population data to develop two separate models. RETFound was tested using diabetic retinopathy databases of MESSIDOR-2, the Indian diabetic retinopathy image , and Kaggle APTOS-2019, which were classified according to the International Clinical Diabetic Retinopathy Severity scale. Cross-evaluation was performed between the three databases and models were fine-tuned using a single dataset before being tested on the other ones.
Study findings With fewer labeled data, the Adapted RETFound model regularly surpassed many comparator models in the diagnosis and prognosis of sight-threatening eye illnesses, as well as the incident prediction of complicated systemic ailments including heart failure and myocardial infarction.
Superior performance was also observed in categorizing several illnesses such as glaucoma. The findings of RETFound AUPR were similarly considerably higher than those of the comparable groups.
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