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

arXiv: https://arxiv.org/abs/2207.04565 · HTML: https://mlsanity.com/paper/2207.04565/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 10 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs](https://mlsanity.com/paper/2106.12930/) (2021)
- [Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning](https://mlsanity.com/paper/2005.14284/) (2020)

## Built on by (most cited first, 2 of 2)

- [Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning](https://mlsanity.com/paper/2303.14615/) (2023)
- [Robust Internal Representations for Domain Generalization](https://mlsanity.com/paper/2309.15522/) (2023)

## Abstract (arXiv)

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.