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

Can deep learning reliably distinguish optic nerve head atrophy from glaucoma, nonarteritic anterior ischemic optic neuropathy, and optic neuritis as well as healthy eyes?In this cross-sectional study, a ResNet-3D model analyzing the entire optical coherence tomography (OCT) volume reached 88.9% accuracy, while models analyzing the peripapillary or optic nerve head (ONH) region only attained 85.9% and 87.0% accuracy, respectively (F1 scores, 0.71-0.94), indicating that atrophy signatures reside both within and beyond the ONH.Automated volumetric deep learning analysis of unsegmented OCT scans may enhance diagnostic accuracy for ONH atrophy, helping clinicians differentiate these conditions more reliably and potentially guiding evaluation and intervention.

Corresponding Author: Mark Kupersmith, MD, One Gustave Levy Place, 10th Floor, INN, New York, NY 10029 (mark.kupersmith@mountsinai.org).

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