Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence.

ABSTRACT:

Purpose

To evaluate the benefit of using OPTIMEyes, an Interactive Machine Learning (IML) platform for annotation of ophthalmology images, along three axes: efficiency, uniformity, and noninferiority when compared to unassisted segmentation of geographic atrophy (GA) from fundus autofluorescence.

Methods

We enrolled 10 annotators and collected 110 fundus autofluorescence images from patients with GA. We obtained ground truth GA segmentations from an expert retinal specialist and used 100 images to train the OPTIMEyes Artificial Intelligence (AI) segmentation model and 10 images to compare unassisted segmentation with AI-assisted segmentation. During data analysis, we excluded two annotators due to highly inconsistent segmentations. We measured annotation time in seconds and

SUBMITTER: Bearce B 

PROVIDER: S-EPMC12805964 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature