The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.

ABSTRACT:

Background

The purpose of this study is to develop an artificial intelligence (AI)-based automated diabetic retinopathy (DR) grading and training system from a real-world diabetic dataset of China, and in particular, to investigate its effectiveness as a learning tool of DR manual grading for medical students.

Methods

We developed an automated DR grading and training system equipped with an AI-driven diagnosis algorithm to highlight highly prognostic related regions in the input image. Less experienced prospective physicians received pre- and post-training tests by the AI diagnosis platform. Then, changes in the diagnostic accuracy of the participants were evaluated.

Results

We randomly selected 8,063 cases diagnosed with DR and 7,925 with non-DR fundus images from ty

SUBMITTER: Qian X 

PROVIDER: S-EPMC9678340 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature