首个眼底超声视频数据集，助力自动判断视网膜脱离及黄斑状态
ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound
- 构建首个公开眼底超声视频数据集，标注视网膜脱离与黄斑状态
- 训练40个模型，3D卷积与Transformer表现最佳，准确率达90%以上
- 适合眼科AI研发者、超声诊断辅助系统开发者使用
视网膜脱离（RD）是威胁视力的急症，治疗紧迫性与预后关键在于黄斑是否受累——黄斑是否完整。床旁超声（POCUS）是一种快速、无创且成本低的影像工具，广泛用于各类临床场景中检测RD。然而，其诊断效果受限于专家解读需求，尤其在资源匮乏地区。深度学习有望实现超声图像上RD的自动化检测，但目前尚无临床可用模型，且此前研究未涉及黄斑状态这一对手术优先级至关重要的判别。此外，尚无公开数据集支持基于超声视频的黄斑相关RD分类。本文提出眼底超声数据集ERDES，是首个开放获取的眼部超声视频数据集，标注了（i）RD存在与否，以及（ii）黄斑脱离或黄斑完整状态。ERDES可推动深度学习在RD检测中的发展。我们还通过8种架构训练40个模型，包括3D卷积网络和基于Transformer的模型，提供基准性能。
原文摘要 · Abstract (English)
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.
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