Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.

ABSTRACT:

Background

Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy.

Methods

A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with

SUBMITTER: Tabuchi H 

PROVIDER: S-EPMC11503156 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature