[2508.04735] ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound

Quantitative Biology > Quantitative Methods arXiv:2508.04735 (q-bio) [Submitted on 5 Aug 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound Authors:Yasemin Ozkut, Pouyan Navard, Srikar Adhikari, Elaine Situ-LaCasse, Josie Acuña, Adrienne Yarnish, Alper Yilmaz View a PDF of the paper titled ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound, by Yasemin Ozkut and 6 other authors View PDF HTML (experimental) 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 (ERDE...