Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data
This addresses the need for reliable clinical decision support in eye care to handle increasing demand, though it is incremental as it validates an existing method on new data.
The study validated Pegasus-OCT, a deep learning software for detecting retinal diseases like AMD and DME from macular OCT scans, achieving AUROCs of at least 98-99% across independent, multi-centre datasets.
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 detection were found to be at least 99% and 98%, respectively. Conclusions: The ability of a clinical decision support system to cater for different populations is key to its adoption. Pegasus-OCT was shown to be able to detect AMD, DME and general anomalies in OCT volumes acquired across multiple independent sites with high performance. Its use thus offers substantial promise, with the potential to alleviate the burden of growing demand in eye care services caused by retinal disease.