Validation of artificial intelligence algorithm LuxIA for screening of diabetic retinopathy from a single 45° retinal colour fundus images: the CARDS study.

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.