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Purpose: To evaluate Pegasus-OCT, a clinical decision support software forthe identification of features of retinal disease from macula OCT scans, acrossheterogenous populations involving varying patient demographics, devicemanufacturers, 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 thesoftware. Results were evaluated against ground truth provided by the datasetowners.  Results: Pegasus-OCT performed with AUROCs of at least 98% for all datasetsin the detection of general macular anomalies. For scans of sufficient quality,the AUROCs for general AMD and DME detection were found to be at least 99% and98%, respectively.  Conclusions: The ability of a clinical decision support system to cater fordifferent populations is key to its adoption. Pegasus-OCT was shown to be ableto detect AMD, DME and general anomalies in OCT volumes acquired acrossmultiple independent sites with high performance. Its use thus offerssubstantial promise, with the potential to alleviate the burden of growingdemand in eye care services caused by retinal disease.

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