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

arXiv: https://arxiv.org/abs/2508.04735 · HTML: https://mlsanity.com/paper/2508.04735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?](https://mlsanity.com/paper/2004.04968/) (2020)
- [A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning](https://mlsanity.com/paper/2407.18667/) (2024)

## Abstract (arXiv)

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.