AI工具提升糖尿病视网膜病变筛查效率，减少医生工作量且不漏诊危重病例。
Improving diabetic retinopathy screening using Artificial Intelligence: design, evaluation and before-and-after study of a custom development
- 开发一体化AI系统NaIA-RD，同步评估眼底病与图像质量。
- 筛查后敏感度提升，误判率仅10%以下，可减少4.27倍影像分析负担。
- 适合基层医疗推广，助力临床路径长期优化。
背景：人工智能辅助的糖尿病视网膜病变（DR）筛查可预防最严重后果。在西班牙纳瓦拉大学医院（HUN），全科医生（GPs）负责分级眼底图像，并将高风险患者转诊至眼科医生。方法：基于医生需求，HUN开发定制化AI工具NaIA-RD，用于协助其开展筛查。本文介绍其设计、实现及独特之处——单系统同时完成DR分级与图像质量评估。通过前所未有的前后对比研究，比较了使用前（19,828例）和使用后（22,962例）的筛查数据。结果：NaIA-RD影响了3/4全科医生的筛查标准，提高敏感性；对非转诊建议的一致性达94.6%以上，但对转诊建议一致性较低且波动大（23.4%~86.6%）。眼科医生在93%的争议转诊案例中认定为需转诊，证实了部分错误判断。在独立运行模式下，该系统可使影像可视化工作量降低4.27倍，且未遗漏任何由全科医生识别出的威胁视力的病例。结论：在支持下，DR筛查更高效，且可在无监督情况下独立承担第一级筛查任务。这表明，当AI无缝融入临床流程时，能长期优化诊疗路径。
原文摘要 · Abstract (English)
Background: The worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients. Methods: After collecting their requirements, HUN decided to develop a custom AI tool, called NaIA-RD, to assist their GPs in DR screening. This paper introduces NaIA-RD, details its implementation, and highlights its unique combination of DR and retinal image quality grading in a single system. Its impact is measured in an unprecedented before-and-after study that compares 19,828 patients screened before NaIA-RD's implementation and 22,962 patients screened after. Results: NaIA-RD influenced the screening criteria of 3/4 GPs, increasing their sensitivity. Agreement between NaIA-RD and the GPs was high for non-referral proposals (94.6% or more), but lower and variable (from 23.4\% to 86.6%) for referral proposals. An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93% of a sample of them as referable. In an autonomous setup, NaIA-RD would have reduced the study visualization workload by 4.27 times without missing a single case of sight-threatening DR referred by a GP. Conclusion: DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening. This shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.
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