Ultra-widefield (UWF) imaging is a promising modality that captures a larger
retinal field of view compared to traditional fundus photography. Previous
studies showed that deep learning (DL) 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. We first improve on the state of
the field by proposing a DL model that can recognise multiple retinal diseases
under more realistic conditions. 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 curve (AUC) of 0.9206 on an internal test set, and an AUC of
0.9841 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 to having the full images
available.

我们使用全局可解释性方法建立了一个多疾病的深度学习模型来检测超广角成像中的视网膜疾病，发现在这些图像中最重要的区域是后极区域，只利用后极区域周围 10% 的图像即可实现与使用整个图像相当的性能。