系统梳理糖尿病视网膜病变AI筛查十年演进，打通从像素到临床落地的路径。
From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening
- 整合50+研究与20+数据集，分析自监督、联邦学习等关键方法进展
- 揭示多中心验证与临床信任仍是落地主要障碍
- 适合关注医疗AI可复现性与部署落地的研究者参考
糖尿病视网膜病变（DR）是导致可预防失明的主要原因，早期检测对全球减少视力丧失至关重要。过去十年，深度学习推动了DR筛查的变革，从基于私有数据集的早期卷积神经网络，发展到解决类别不平衡、标签稀缺、域偏移和可解释性等问题的先进流程。本综述首次系统性总结2016-2025年间的研究进展，整合50余项研究与超过20个数据集的结果。我们批判性分析自监督、半监督学习、域泛化、联邦训练及混合神经符号模型等方法演进，同时探讨评估协议、报告标准与可复现性挑战。基准表格对比不同数据集上的性能表现，讨论指出多中心验证与临床信任仍存在显著空白。通过连接技术进步与转化障碍，本文提出可复现、隐私保护且临床可用的DR AI实践路径。这些创新不仅适用于DR，也可广泛推广至大规模医学影像领域。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning has transformed DR screening, progressing from early convolutional neural networks trained on private datasets to advanced pipelines addressing class imbalance, label scarcity, domain shift, and interpretability. This survey provides the first systematic synthesis of DR research spanning 2016-2025, consolidating results from 50+ studies and over 20 datasets. We critically examine methodological advances, including self- and semi-supervised learning, domain generalization, federated training, and hybrid neuro-symbolic models, alongside evaluation protocols, reporting standards, and reproducibility challenges. Benchmark tables contextualize performance across datasets, while discussion highlights open gaps in multi-center validation and clinical trust. By linking technical progress with translational barriers, this work outlines a practical agenda for reproducible, privacy-preserving, and clinically deployable DR AI. Beyond DR, many of the surveyed innovations extend broadly to medical imaging at scale.
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