Why carry out this study?

Compared with conventional color fundus photography, ultra-widefield images (UWFIs) have the advantages of no pupil dilatation, a wide imaging range, and fast acquisition, which are suitable for fundus disease screening.

We aim to develop a more clinically applicable deep learning model for multidisease classification, as few studies have done so previously.

What was learned from the study?

The UWFI multidisease classification model that we designed on the basis of a small sample size performed well with fast model inference. Its performance was comparable to that of doctors with 2–5 years of experience at a tertiary referral hospital.

The model is expected to be applied in developing countries and remote areas to assist in fundus disease screening.

Further expansion of the sample size and number of diseases and optimization of the model are needed in the future to achieve higher performance.