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

Retinal detachment (RD) is a vision-threatening condition requiring 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 across 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 no clinically available models exist, 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. For code, pre-trained models, and training scripts, see: https://github.com/OSUPCVLab/ERDES.视网膜脱离（Retinal detachment, RD）是一类可导致视力丧失的急症，需及时干预以保留视功能。影响治疗紧迫性与视觉预后的核心因素为黄斑受累状态——即黄斑是否发生脱离。床旁超声（Point-of-care ultrasound, POCUS）是一种快速、无创且兼具成本效益的成像手段，在各类临床场景中被广泛用于视网膜脱离的检测。但该技术的诊断效能受限于需专业人员解读的瓶颈，在资源匮乏的医疗环境中这一问题尤为突出。深度学习技术有望实现超声图像中视网膜脱离的自动化检测，但目前尚无临床可用的相关模型，且既往研究未覆盖黄斑状态这一决定手术优先级的关键区分要素。此外，当前尚无公开数据集可用于基于黄斑状态的视网膜脱离超声视频分类。本研究推出视网膜脱离超声数据集（Eye Retinal DEtachment ultraSound, ERDES）——首个针对眼部超声视频片段进行标注的开放获取数据集，标注维度包含：(i) 视网膜脱离的存在与否，(ii) 黄斑脱离与黄斑完整的状态区分。ERDES可为视网膜脱离检测的机器学习模型开发提供数据支撑。我们还通过在8种架构（包括三维卷积神经网络与基于Transformer的模型）上训练40个模型，提供了基准评测结果。如需获取代码、预训练模型与训练脚本，请访问：https://github.com/OSUPCVLab/ERDES.