ERDES: Eye Retinal Detachment Ultrasound Dataset

📌 Introduction

ERDES is a large-scale, publicly available dataset of 3D ocular ultrasound videos for retinal and macular detachment classification. It was introduced in our paper ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound. The corpus consists of 5,381 expertly annotated video clips totaling to 5 hours and 10 minutes, providing a valuable resource for medical AI research in ophthalmology.

Key Features:

✅ 5381 labeled ultrasound video clips
✅ Expert annotations for retinal detachment (RD) and macular status
✅ Structured classification (Normal, RD, PVD, macula-detached/intact)
✅ Preprocessed for privacy and consistency

---

🎯 Motivation

Medical video datasets for AI are scarce despite their clinical importance. ERDES bridges this gap by offering:

• A standardized benchmark for retinal detachment classification in ultrasound videos.
• Support for spatiotemporal analysis (e.g., 3D CNNs).
• Open access to accelerate research in ocular diagnostics.

---

📊 Dataset Overview

1. Data Structure

Videos are categorized into:

• Normal
• Retinal Detachment (RD)
  • Macula-detached:
    • Bilateral
    • Temporal detachment
  • Macula-intact:
    • Nasal
    • Temporal detachment
• Posterior Vitreous Detachment (PVD)

Folder structure reflects labels:

2. Annotations

Each clip is labeled by sonologists for:

• Presence/absence of retinal detachment.
• Macular involvement (detached/intact).

3. Preprocessing

• Privacy: PHI removed using YOLO-based globe detection.
• Consistency: Cropped to the ocular ROI.
• Format: MP4 videos.

---

📥 Download

Access the dataset via the HuggingFace API:

from datasets import load_dataset

dataset = load_dataset("pnavard/erdes")

---

🛠️ Code & Baselines

Repository: we open source our baseline experimetns on our GitHub repo. Which includes:

• Baseline 3D CNN and ViT models for classification.
• End-to-end diagnostic pipeline for macular detachment.

---

📜 Citation

If you use ERDES, cite:

@inproceedings{navardocular,
  title={A Benchmark Dataset for Retinal Detachment Classification in Spatiotemporal Ocular Ultrasound},
  author={Navard, Pouyan and Ozkut, Yasemin and Adhikari, Srikar and Yilmaz, Alper},
  booktitle={Nature Scientific Data (Under Review)},
  year={2025}
}