Optic Nerve Atrophy Conditions Associated With 3D Unsegmented Optical Coherence Tomography Volumes Using Deep Learning.

IMPORTANCE

Accurate differentiation of optic nerve head (ONH) atrophy is vital for guiding diagnosis and treatment of conditions such as glaucoma, nonarteritic anterior ischemic optic neuropathy (NAION), and optic neuritis. Traditional 2-dimensional assessments may overlook subtle, volumetric changes.

OBJECTIVE

To determine whether a 3-dimensional (3D) deep learning model trained on unsegmented ONH optical coherence tomography (OCT) scans can reliably distinguish optic atrophy in glaucoma, NAION, optic neuritis, and healthy eyes. DESIGN, SETTING,

AND PARTICIPANTS

This cross-sectional study used data from multiple clinical trials and referral centers (2008-2025), including randomized trials, longitudinal studies, and referral clinics. Participants included patients with glaucoma, NAION, or optic neuritis and healthy control patients.

EXPOSURES

Three ResNet-3D-18 models were trained using 5-fold stratified cross-validation. One assessed the full OCT volume, another focused only on the peripapillary region (PPR), and the third considered only the ONH. Identical data splits were used to allow direct performance comparison.

MAIN OUTCOMES AND MEASURES

Classification accuracy, macro area under the receiver operating characteristic curve (AUC-ROC), precision, recall, and F1 scores, aggregated across all validation folds. Confusion matrices were generated to characterize misclassifications.

RESULTS

A total of 7014 Cirrus ONH OCT scans from 1382 eyes of glaucoma (n = 113), NAION (n = 311), optic neuritis (n = 163), and healthy controls (n = 715) were analyzed. The mean (SD) age was 54.2 (16.9) years; there were 733 (65%) male patients and 402 (35%) female patients. The entire-volume model achieved 88.9% accuracy (macro AUC-ROC, 0.977; 95% CI, 0.974-0.979) and F1 scores of 0.94, 0.87, 0.78, and 0.91 for glaucoma, NAION, optic neuritis, and healthy eyes, respectively. The PPR-only model reached 85.9% accuracy (AUC-ROC, 0.970; 95% CI, 0.967-0.972), while the ONH-only model attained 87.0% accuracy (AUC-ROC, 0.972; 95% CI, 0.970-0.975). Both achieved F1 scores from 0.71 to 0.94. Optic neuritis presented the greatest classification challenge, misclassified as NAION or healthy when axonal loss was severe or minimal. Activation maps revealed disease-specific regions of interest in the retina, including the retinal nerve fiber layer, ganglion cell layer, and retinal pigment epithelium.

CONCLUSIONS AND RELEVANCE

Deep learning-based analysis of unsegmented OCT scans reliably distinguished between different forms of optic nerve atrophy, suggesting subtle, disease-specific structural patterns. This automated approach may support diagnostic efforts, guide clinical management of optic neuropathies, and complement less standardized imaging modalities and subjective clinical impressions.