Classifying the severity of diabetic macular oedema from optical coherence tomography scans using deep learning: a feasibility study.

BackgroundDiabetic macular oedema (DME) is a vision-threatening complication of diabetes mellitus. It is reliably detected using optical coherence tomography (OCT). This work evaluates a deep learning system (DLS) for the automated detection and classification of DME severity from OCT images. MethodsAnonymised OCT images were retrospectively obtained from 950 patients at University Hospital Galway, Ireland. Images were graded by a consultant ophthalmologist to classify the level of DME present (normal, non-centre-involving DME, centre-involving DME) excluding other pathologies. A DLS was trained using cross-validation, then evaluated on a test dataset and an external dataset. The test set was graded by a second ophthalmologist for comparison. ResultsIn detecting the presence of DME, the DLS achieved a mean area under the receiver operating characteristic curve (AUC) of 0.98 on cross-validation. AUCs of 0.94 (95% CI 0.90-0.98) and 0.94 (0.92-0.96) were achieved on evaluation of DME detection for the test dataset when graded by the first and second ophthalmologist respectively. An AUC of 0.94 (0.92-0.96) was achieved on evaluation with the external dataset. When detecting the DME severity, AUCs of 0.98, 0.86 and 0.99 were achieved per class on cross validation. For the test dataset, AUCs of 0.99, 0.89 and 0.98 were achieved when graded by the first ophthalmologist and AUCs of 0.96, 0.89 and 0.95 were achieved when graded by the second ophthalmologist. ConclusionThis study suggests promising results for the use of deep learning in the classification of severity of DME which could be used to automate screening for DME and direct appropriate referrals.