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

TL;DR

This paper introduces a deep learning algorithm for automatic detection of pediatric papilledema in fundus images, achieving accuracy comparable to experts, and emphasizes explainability and robustness in clinical diagnosis.

Contribution

The study presents a novel deep learning approach that localizes the optic disc and detects explainable indicators of papilledema, improving diagnostic accuracy in pediatric fundus images.

Findings

Achieved diagnostic accuracy comparable to ophthalmologists

Effective in presence of pseudopapilledema

Utilizes data augmentation for robustness

Abstract

Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists.