开发手机应用，自动分析眼底照片变化，助力早期发现眼部疾病。
Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images
- 通过移动端上传眼底图像，自动计算血管扭曲度等指标。
- 在Messidor和MAPLES-DR数据集上验证了病变分级与黄斑水肿风险预测性能。
- 适合关注眼健康、需长期监测的老年人群使用。
机器学习在医学影像诊断中日益受到关注，尤其在眼底图像分析方面。然而，该技术尚未实现临床独立应用，仍依赖专业人员人工判断。为此，我们设计了一款移动应用，用于监测与年龄相关眼病相关的视网膜眼底图像指标。平台旨在通过定期上传图像，追踪这些指标随时间的变化趋势，提供潜在眼病的早期预警，而非直接给出诊断结论。分析的指标包括血管扭曲度，以及青光眼、视网膜病变和黄斑水肿的征兆。为评估视网膜病变等级及黄斑水肿风险，模型在Messidor数据集上训练，并与在MAPLES-DR数据集上训练的同类模型进行对比。此外，整合DeepSeeNet青光眼检测模型信息及血管扭曲度计算结果，构建综合眼底图像监测平台。最终，该应用可实现对与年龄相关眼病指标的周期性监控，及时发现异常变化。
原文摘要 · Abstract (English)
Machine learning is gaining significant attention as a diagnostic tool in medical imaging, particularly in the analysis of retinal fundus images. However, this approach is not yet clinically applicable, as it still depends on human validation from a professional. Therefore, we present the design for a mobile application that monitors metrics related to retinal fundus images correlating to age-related conditions. The purpose of this platform is to observe for a change in these metrics over time, offering early insights into potential ocular diseases without explicitly delivering diagnostics. Metrics analysed include vessel tortuosity, as well as signs of glaucoma, retinopathy and macular edema. To evaluate retinopathy grade and risk of macular edema, a model was trained on the Messidor dataset and compared to a similar model trained on the MAPLES-DR dataset. Information from the DeepSeeNet glaucoma detection model, as well as tortuosity calculations, is additionally incorporated to ultimately present a retinal fundus image monitoring platform. As a result, the mobile application permits monitoring of trends or changes in ocular metrics correlated to age-related conditions with regularly uploaded photographs.
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