1. Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data

    Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data

    Purpose: To evaluate Pegasus-OCT, a clinical decision support software for the identification of features of retinal disease from macula OCT scans, across heterogenous populations involving varying patient demographics, device manufacturers, acquisition sites and operators. Methods: 5,588 normal and anomalous macular OCT volumes (162,721 B-scans), acquired at independent centres in five countries, were processed using the software. Results were evaluated against ground truth provided by the dataset owners. Results: Pegasus-OCT performed with AUROCs of at least 98% for all datasets in the detection of general macular anomalies. For scans of sufficient quality, the AUROCs for general AMD and DME ...

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