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

Benjamin Bearce¹, Steve McNamara¹, Scott Kinder¹

¹Department of Ophthalmology, University of Colorado Anschutz, Aurora, CO, USA.

Translational Vision Science & Technology

|January 14, 2026

Summary

This summary is machine-generated.

The OPTIMEyes platform significantly improves geographic atrophy (GA) segmentation from fundus autofluorescence images, reducing annotation time and enhancing segmentation consistency among annotators without compromising accuracy.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Geographic atrophy (GA) segmentation from fundus autofluorescence is crucial for monitoring disease progression.
Manual segmentation is time-consuming and can suffer from inter-annotator variability.

Purpose of the Study:

To evaluate the OPTIMEyes Interactive Machine Learning (IML) platform for ophthalmology image annotation.
Assess efficiency, uniformity, and noninferiority of AI-assisted segmentation compared to unassisted segmentation of geographic atrophy (GA).

Main Methods:

10 annotators segmented 110 fundus autofluorescence images of GA.
Ground truth segmentations were provided by an expert retinal specialist.
Annotation time and DICE scores were used to compare unassisted and AI-assisted segmentation.

Main Results:

AI assistance reduced average annotation time by 96 seconds.
OPTIMEyes improved segmentation similarity across annotators (mean DICE difference 0.02).
Significant improvements in segmentation quality were observed for challenging images (mean DICE improvement: 0.38).

Conclusions:

OPTIMEyes enhances GA segmentation efficiency and uniformity in fundus autofluorescence images.
The platform shortens annotation time and improves inter-annotator agreement without sacrificing segmentation quality.
OPTIMEyes facilitates the creation of high-quality labeled datasets for research and clinical applications.