Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.

ABSTRACT:

Purpose

This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis.

Methods

We used Illinois Eye and Ear Infirmary fundus data set as an instance of RWD in addition to six publicly available fundus data sets. We compared the performance of DL-trained models on public data and RWD for glaucoma classification and optic disc (OD) segmentation tasks. For each task, we created models trained on each data set, respectively, and each model was tested on both data sets. We further examined each model's decision-making process and learned embeddings for the glaucoma classification task.

Results

Using public data for the test set, public-trained models

SUBMITTER: Rashidisabet H 

PROVIDER: S-EPMC10629532 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature