Abreu-Gonzalez Rodrigo, Susanna-González Gabriela, Blair Joseph P M, Lasagni Vitar Romina M, Ciller Carlos, Apostolopoulos Stefanos, De Zanet Sandro, Rodríguez Martín José Natán, Bermúdez Carlos, Calle Pascual Alfonso Luis, Rigo Elena, Cervera Taulet Enrique, Escobar-Barranco Jose Juan, Cobo-Soriano Rosario, Donate-Lopez Juan
Ophthalmology, University Hospital of La Candelaria, La Matanza, Spain
Fundación VerSalud, Madrid, Spain.
BMJ Open Ophthalmol. 2025 May 8;10(1):e002109. doi: 10.1136/bmjophth-2024-002109.
This study validated the artificial intelligence (AI)-based algorithm LuxIA for screening more-than-mild diabetic retinopathy (mtmDR) from a single 45° colour fundus image of patients with diabetes mellitus (DM, type 1 or type 2) in Spain. Secondary objectives included validating LuxIA according to the International Clinical Diabetic Retinopathy (ICDR) classification and comparing its performance between different devices.
In this multicentre, cross-sectional study, retinal colour fundus images of adults (≥18 years) with DM were collected from five hospitals in Spain (December 2021-December 2022). 45° colour fundus photographs were captured using non-mydriatic Topcon and ZEISS cameras. The Discovery platform (RetinAI) was used to collect images. LuxIA output was an ordinal score (1-5), indicating a classification as mtmDR based on an ICDR severity score.
945 patients with DM were included; the mean (SD) age was 64.6 (13.5) years. The LuxIA algorithm detected mtmDR with a sensitivity and specificity of 97.1% and 94.8%, respectively. The area under the receiver-operating characteristic curve was 0.96, indicating a high test accuracy. The 95% CI data for overall accuracy (94.8% to 95.6%), sensitivity (96.8% to 98.2%) and specificity (94.3% to 95.1%) indicated robust estimations by LuxIA, which maintained a concordance of classification (N=829, kappa=0.837, p=0.001) when used to classify Topcon images. LuxIA validation on ZEISS-obtained images demonstrated high accuracy (90.6%), specificity (92.3%) and lower sensitivity (83.3%) as compared with Topcon-obtained images.
AI algorithms such as LuxIA are increasing testing feasibility for healthcare professionals in DR screening. This study validates the real-world utility of LuxIA for mtmDR screening.
本研究验证了基于人工智能（AI）的算法LuxIA，用于从西班牙1型或2型糖尿病（DM）患者的单张45°彩色眼底图像中筛查非轻度糖尿病视网膜病变（mtmDR）。次要目标包括根据国际临床糖尿病视网膜病变（ICDR）分类验证LuxIA，并比较其在不同设备之间的性能。
在这项多中心横断面研究中，收集了西班牙五家医院（2021年12月至2022年12月）成年（≥18岁）DM患者的视网膜彩色眼底图像。使用免散瞳的拓普康和蔡司相机拍摄45°彩色眼底照片。利用Discovery平台（RetinAI）收集图像。LuxIA的输出为一个序数评分（1 - 5），表示基于ICDR严重程度评分的mtmDR分类。
纳入945例DM患者；平均（标准差）年龄为64.6（13.5）岁。LuxIA算法检测mtmDR的灵敏度和特异度分别为97.1%和94.8%。受试者操作特征曲线下面积为0.96，表明测试准确性高。总体准确性（94.8%至95.6%）、灵敏度（96.8%至98.2%）和特异度（94.3%至95.1%）的95%置信区间数据表明LuxIA的估计可靠，在用于分类拓普康图像时，其分类一致性良好（N = 829，kappa = 0.837，p = 0.001）。与拓普康获取的图像相比，LuxIA对蔡司获取图像的验证显示出较高的准确性（90.6%）、特异度（92.3%）和较低的灵敏度（83.3%）。
诸如LuxIA之类的AI算法正在提高医疗保健专业人员在糖尿病视网膜病变筛查中的检测可行性。本研究验证了LuxIA在mtmDR筛查中的实际应用价值。
