{"ID":2886596,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04735","arxiv_id":"2508.04735","title":"ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound","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.","short_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 imagi...","url_abs":"https://arxiv.org/abs/2508.04735","url_pdf":"https://arxiv.org/pdf/2508.04735v2","authors":"[\"Yasemin Ozkut\",\"Pouyan Navard\",\"Srikar Adhikari\",\"Elaine Situ-LaCasse\",\"Josie Acuña\",\"Adrienne Yarnish\",\"Alper Yilmaz\"]","published":"2025-08-05T21:55:54Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}