ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound

TL;DR

This paper introduces ERDES, the first open-access ocular ultrasound video dataset labeled for retinal detachment and macular status, enabling development of deep learning models for automated diagnosis.

Contribution

The paper presents ERDES, a novel dataset for retinal detachment and macular status classification, and provides baseline deep learning benchmarks for this task.

Findings

ERDES dataset enables machine learning research in retinal detachment detection.

Baseline models achieve promising performance on RD and macular status classification.

The dataset supports development of automated, non-invasive diagnostic tools for ocular emergencies.

Abstract

Retinal detachment (RD) is a vision-threatening condition that requires prompt intervention to preserve sight. A critical factor in treatment urgency and visual prognosis is macular involvement -- whether the macula is intact or detached. Point-of-care ultrasound (POCUS) is a fast, non-invasive and cost-effective imaging tool commonly used to detect RD in various clinical settings. However, its diagnostic utility is limited by the need for expert interpretation, especially in resource-limited environments. Deep learning has the potential to automate RD detection on ultrasound, but there are no clinically available models, and prior research has not addressed macular status -- an essential distinction for surgical prioritization. Additionally, no public dataset currently supports macular-based RD classification using ultrasound video. We introduce Eye Retinal DEtachment ultraSound (ERDES), the first open-access dataset of ocular ultrasound clips labeled for (i) presence of RD and (ii) macula-detached vs. macula-intact status. ERDES enables machine learning development for RD detection. We also provide baseline benchmarks by training 40 models across eight architectures, including 3D convolutional networks and transformer-based models.