Key result
DeepSLE detects systemic lupus erythematosus from retinal images with AUC up to ~0.97.
Why the study?
Screening for systemic lupus erythematosus and related complications poses significant challenges globally due to complex diagnostic criteria and public unawareness, while retinal involvement could provide insights into disease activity and severity.
Does a deep learning system (DeepSLE) improve the detection of systemic lupus erythematosus from retinal images compared to primary care physicians?
Observational
Yes
Does a deep learning system (DeepSLE) improve the detection of systemic lupus erythematosus from retinal images compared to primary care physicians?
A deep learning system applied to retinal images can accurately detect systemic lupus erythematosus and its complications, outperforming primary care physicians in sensitivity.
Systemic lupus erythematosus (SLE) is a serious autoimmune disorder predominantly affecting women. However, screening for SLE and related complications poses significant challenges globally, due to complex diagnostic criteria and public unawareness. Since SLE-related retinal involvement could provide insights into disease activity and severity, we develop a deep learning system (DeepSLE) to detect SLE and its retinal and kidney complications from retinal images. In multi-ethnic validation datasets comprising 247,718 images from China and UK, DeepSLE achieves areas under the receiver operating characteristic curve of 0.822-0.969 for SLE. Additionally, DeepSLE demonstrates robust performance across subgroups stratified by gender, age, ethnicity, and socioeconomic status. To ensure DeepSLE's explainability, we conduct both qualitative and quantitative analyses. Furthermore, in a prospective reader study, DeepSLE demonstrates higher sensitivities compared with primary care physicians. Altogether, DeepSLE offers digital solutions for detecting SLE and related complications from retinal images, holding potential for future clinical deployment.
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Li et al. (2025) conducted an observational in Systemic lupus erythematosus (SLE). DeepSLE (deep learning system) vs. Primary care physicians was evaluated on Detection of SLE (AUC 0.822-0.969). DeepSLE, a deep learning system, detected systemic lupus erythematosus from retinal images with an area under the receiver operating characteristic curve of 0.822-0.969 in multi-ethnic datasets.
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