Data Sheet 1_Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development.pdf

BackgroundThe 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.
MethodsAfter 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.
ResultsNaIA-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.
ConclusionDR 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.

背景 糖尿病视网膜病变（diabetic retinopathy, DR）最严重的不良预后可通过人工智能（AI）辅助的糖尿病视网膜病变筛查项目得以预防。西班牙纳瓦拉大学附属医院（University Hospital of Navarre, HUN）正在开展一项糖尿病视网膜病变筛查项目，由全科医生（general practitioners, GPs）对眼底图像进行分级，并将目标患者转诊至由眼科医生执行的二级筛查环节。

方法 纳瓦拉大学附属医院在收集临床需求后，决定开发一款名为NaIA-RD的定制化人工智能工具，以辅助其全科医生开展糖尿病视网膜病变筛查工作。本文详细介绍了NaIA-RD的研发与部署细节，并着重阐述了其将糖尿病视网膜病变分级与眼底图像质量评估整合于单一系统的独特优势。本研究通过一项前所未有的前后对照研究评估其应用效果，对比了NaIA-RD部署前筛查的19828例患者与部署后筛查的22962例患者的筛查数据。

结果 NaIA-RD对四分之三的全科医生的筛查判定标准产生了影响，提升了其筛查灵敏度。在非转诊建议方面，NaIA-RD与全科医生的评估一致性较高（达94.6%及以上）；但在转诊建议方面，一致性较低且波动范围较大（23.4%至86.6%）。针对二者评估存在分歧的转诊建议，眼科医生对其中93%的样本进行标注后判定为需转诊，由此推翻了多数NaIA-RD的误判结果。在独立运行模式下，NaIA-RD可将人工阅片工作量降低4.27倍，且未漏诊任何1例全科医生转诊的威胁视力型糖尿病视网膜病变病例。

结论 借助NaIA-RD辅助的糖尿病视网膜病变筛查更为高效，该工具可安全地独立完成一级筛查工作。本研究证实，当人工智能设备与临床工作流程无缝整合后，可长期助力优化临床诊疗路径。