Detection of macular neovascularization in eyes presenting with macular edema using optical coherence tomography angiography and a deep-learning model.

Abstract

Purpose: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with optical coherence tomography (OCT) and OCT angiography(OCTA) in eyes with macular edema from various diagnoses.

Design: Prospective cross-sectional study.

Participants: Study participants with macular edema due to either treatment-naïve exudative age-related macular degeneration (AMD), diabetic macular edema (DME), or retinal vein occlusion (RVO).

Methods: Study participants were imaged with macular 3x3-mm and 6x6-mm spectral-domain OCTA. Eyes with exudative AMD were required to have MNV in the central 3x3-mm area. A previously developed hybrid multi-task convolutional neural network for MNV detection (aiMNV) and segmentation was applied to all images, regardless of image quality.

Main outcome measures: Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of detecting MNV; and intersection over union(IoU) score and F1 score for segmentation.

Results: Of 114 eyes from 112 study participants, 56 eyes had MNV due to exudative AMD and 58 eyes with macular edema due to either DME or RVO. 3x3-mm OCTA scans with aiMNV detected MNV with 96.4% sensitivity, 98.3% specificity, 98.2% PPV, and 96.6% NPV. For segmentation, the average IoU score was 0.947 and the F1 score was 0.973. 6x6-mm scans performed well; however, sensitivity for MNV detection was lower than 3x3-mm scans due to lower scan sampling density.

Conclusion: This novel aiMNV algorithm can accurately detect and segment MNV in eyes with exudative AMD from a control group of eyes that present with macular edema from either DME or RVO. Higher scan sampling density improved the aiMNV sensitivity for MNV detection.