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Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared with traditional fundus photography. Previous studies have shown that deep learning models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. Here we first improve on the state of the field by proposing a deep learning model that can recognize multiple retinal diseases under more realistic conditions than what has previously been considered. We then use global explainability methods to identify which regions of the UWF images the model generally attends to. Our model performs very well, separating between healthy and diseased retinas with an area under the receiver operating characteristic curve (AUC) of 0.9196 (±0.0001) on an internal test set, and an AUC of 0.9848 (±0.0004) on a challenging, external test set. When diagnosing specific diseases, the model attends to regions where we would expect those diseases to occur. We further identify the posterior pole as the most important region in a purely data-driven fashion. Surprisingly, 10% of the image around the posterior pole is sufficient for achieving comparable performance across all labels to having the full images available. Advances in ultra-widefield retinal imaging have created a need for automated disease detection. Engelmann and colleagues develop a deep learning model for the detection of retinal diseases. They evaluate it under more realistic conditions than has been considered previously and investigate what regions of ultra-widefield images are important for the performance of such a model.
Engelmann et al. (Thu,) studied this question.