Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral.

ABSTRACT:

Importance

Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets.

Objective

To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models.

Design, setting, and participants

This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images fro

SUBMITTER: Korot E 

PROVIDER: S-EPMC10587830 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature