Code-Free Deep Learning Glaucoma Detection on Color Fundus Images.

ABSTRACT:

Objective

Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study assesses the performance of CFDL in glaucoma detection from fundus images in comparison to expert-designed models.

Design

Deep learning model development, testing, and validation.

Subjects

A total of 101 442 labeled fundus images from the Rotterdam EyePACS Artificial Intelligence for Robust Glaucoma Screening (AIROGS) dataset were included.

Methods

Ophthalmology trainees without coding experience designed a CFDL binary model using the Rotterdam EyePACS AIROGS dataset of fundus images (101 442 labeled images) to differentiate glaucoma from normal optic nerves. We compared our results with bespoke models from the liter

SUBMITTER: Milad D 

PROVIDER: S-EPMC11964632 | biostudies-literature | 2025 Jul-Aug

REPOSITORIES: biostudies-literature