Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

ABSTRACT:

Purpose

Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA.

Methods

Adults with (N = 101) and without (N = 274) DR were recruited from 20 U.S. sites. We collected 3 × 3-mm macular scans using an Optovue RTVue-XR. Rules-based (RB) and deep-learning-based artificial intelligence (AI) algorithms were used to segment the NPA into four anatomical slabs. For comparison, a subset of scans (n = 50) NPA was graded manually.

Results

The AI method outperformed the RB method in intersect

SUBMITTER: Hormel TT 

PROVIDER: S-EPMC11905605 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature