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Papilledema is an ophthalmic neurologic disorder in which increasedintracranial pressure leads to swelling of the optic nerves. Undiagnosedpapilledema in children may lead to blindness and may be a sign oflife-threatening conditions, such as brain tumors. Robust and accurate clinicaldiagnosis of this syndrome can be facilitated by automated analysis of fundusimages using deep learning, especially in the presence of challenges posed bypseudopapilledema that has similar fundus appearance but distinct clinicalimplications. We present a deep learning-based algorithm for the automaticdetection of pediatric papilledema. Our approach is based on optic disclocalization and detection of explainable papilledema indicators through dataaugmentation. Experiments on real-world clinical data demonstrate that ourproposed method is effective with a diagnostic accuracy comparable to expertophthalmologists.

Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning