In Ophthalmology science
PURPOSE : Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.
DESIGN : Cross sectional study.
PARTICIPANTS : Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.
METHODS : The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.
MAIN OUTCOME MEASURES : The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.
RESULTS : For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.
CONCLUSIONS : Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.
FINANCIAL DISCLOSURES : Proprietary or commercial disclosure may be found after the references.
Zang Pengxiao, Hormel Tristan T, Hwang Thomas S, Bailey Steven T, Huang David, Jia Yali
2023-Mar
3D, 3-dimensional, AMD, age-related macular degeneration, AUC, area under the curve, Age-related macular degeneration, CAM, class activation map, DR, diabetic retinopathy, Deep learning, Diabetic retinopathy, Glaucoma, OCT, OCTA, OCT angiography, ROC, receiver operating characteristic
