# Clinician-Led Code-Free Deep Learning for Detecting Papilledema and Pseudopapilledema Using Optic Disc Imaging

## TL;DR

This study shows that code-free deep learning tools can help clinicians distinguish papilledema from normal or pseudopapilledema using OCT images, offering a scalable solution.

## Contribution

The study evaluates code-free AutoML platforms for papilledema detection and severity grading using OCT imaging, showing their clinical feasibility.

## Key findings

- Amazon Rekognition outperformed other platforms in distinguishing papilledema from normal and ODD with an AUC of 0.90.
- AutoML platforms demonstrated high accuracy in grading papilledema severity, with Amazon Rekognition achieving an F1 score of 0.79.
- Code-free deep learning tools show potential for accessible, scalable clinical use in diagnosing papilledema.

## Abstract

Differentiating pseudopapilledema from papilledema is challenging, but critical for prompt diagnosis and to avoid unnecessary invasive procedures. This study evaluates automated machine learning (AutoML) model performance for distinguishing the presence and severity of papilledema using near infrared reflectance images obtained from standard optical coherence tomography, comparing the performance of different AutoML platforms.

A retrospective cohort study was conducted using University Hospitals of Leicester, NHS Trust data. Optic nerve head-centered OCT imaging was obtained for 289 patients (813 images) from 2021 to 2024, with normal optic discs (69 patients, 185 images), papilledema (135 patients, 372 images), and optic disc drusen (ODD) (85 patients, 256 images). AutoML platforms—Amazon Rekognition, Medic Mind, and Google Vertex—were evaluated for (1) distinguishing papilledema from normal discs and ODD and (2) grading papilledema severity (mild/moderate vs. severe). Model performance was evaluated using area under the curve (AUC), precision, recall, F1 score, and confusion matrices for all six models.

Amazon Rekognition showed the best performance in distinguishing papilledema from normal/ODD (AUC, 0.90; F1 score, 0.81) and grading severity of papilledema (AUC, 0.90; F1 score, 0.79), outperforming Google Vertex and Medic Mind, which had slightly lower accuracy and higher misclassification rates.

This evaluation demonstrates the feasibility of AutoML platforms in papilledema classification using near-infrared reflectance images obtained from standard optical coherence tomography. Further external validation is needed to confirm clinical utility.

Automated machine learning can be feasibly used to provide an accessible, scalable solution for clinical teams without coding expertise to recognize and characterize papilledema.