Translational vision science & technology · 2026
Interpretable Deep Learning for OCT-Based Diagnosis of Vitreoretinal Lymphoma Versus Uveitis.
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Khan Azaz, Uner Ogul E, Zang Pengxiao, Rosenberg Christopher R, Suhler Eric B, Lin Phoebe, Flaxel Christina J, Jia Yali
Аннотация
PURPOSE: To develop and validate an interpretable deep learning model that classifies optical coherence tomography (OCT) scans as vitreoretinal lymphoma (VRL) or non-infectious uveitis (NIU)-intermediate, posterior, or panuveitis-and visualizes differentiating pathological OCT features.
METHODS: This cross-sectional study included 45 patients with VRL and 52 with NIU who underwent SPECTRALIS OCT imaging. OCT B-scans were analyzed with a custom-designed deep convolutional neural network classification model combined with gradient-weighted class activation mapping (Grad-CAM). Interpretability was assessed using Grad-CAM heat maps that visualize disease-specific OCT features. We evaluated diagnostic performance at the eye level using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, F1 score, precision, sensitivity, specificity, and kappa.
RESULTS: The model differentiated VRL from NIU at the eye level, with mean ± SD scores of 76.0 ± 8.0 for AUROC, 79.4 ± 10.4 for AUPRC, 75.4 ± 13.3 for accuracy, 74.9 ± 13.7 for F1 score, 73.9 ± 19.3 for precision, 77.7 ± 13.6 for sensitivity, 73.4 ± 20.7 for specificity, and 51.0 ± 26.6 for kappa. Grad-CAM features of VRL included preretinal deposits and changes in the retinal pigment epithelium. In contrast, NIU features included denser vitreous cells and opacities, epiretinal membrane, intraretinal cells, and choroidal and scleral abnormalities consistent with NIU pathophysiology.
CONCLUSIONS: A deep learning model with interpretability can reliably differentiate VRL from NIU, highlighting disease-relevant OCT features.
TRANSLATIONAL RELEVANCE: The proposed deep learning method may serve as a non-invasive triage tool by identifying patients who need confirmatory pars plana vitrectomy.
METHODS: This cross-sectional study included 45 patients with VRL and 52 with NIU who underwent SPECTRALIS OCT imaging. OCT B-scans were analyzed with a custom-designed deep convolutional neural network classification model combined with gradient-weighted class activation mapping (Grad-CAM). Interpretability was assessed using Grad-CAM heat maps that visualize disease-specific OCT features. We evaluated diagnostic performance at the eye level using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, F1 score, precision, sensitivity, specificity, and kappa.
RESULTS: The model differentiated VRL from NIU at the eye level, with mean ± SD scores of 76.0 ± 8.0 for AUROC, 79.4 ± 10.4 for AUPRC, 75.4 ± 13.3 for accuracy, 74.9 ± 13.7 for F1 score, 73.9 ± 19.3 for precision, 77.7 ± 13.6 for sensitivity, 73.4 ± 20.7 for specificity, and 51.0 ± 26.6 for kappa. Grad-CAM features of VRL included preretinal deposits and changes in the retinal pigment epithelium. In contrast, NIU features included denser vitreous cells and opacities, epiretinal membrane, intraretinal cells, and choroidal and scleral abnormalities consistent with NIU pathophysiology.
CONCLUSIONS: A deep learning model with interpretability can reliably differentiate VRL from NIU, highlighting disease-relevant OCT features.
TRANSLATIONAL RELEVANCE: The proposed deep learning method may serve as a non-invasive triage tool by identifying patients who need confirmatory pars plana vitrectomy.