Li Tingyao, Lin Shiqun, Guan Zhouyu, Zhou Yukun, Zeng Dian, Wang Zheyuan, Zhou Yan, Fang Pinqi, Yu Shujie, Liu Ruhan, Chen Xiang, Wang Yan-Ran Joyce, Lu Yuwei, Shu Jia, Qin Yiming, Wu Yiting, Wu Yilan, Wu Chan, Zhang Shangzhu, Shen Jie, Li Huating, Chen Tingli, Li Jin, Tham Yih-Chung, Sabanayagam Charumathi, Zheng Ying Feng, Wagner Siegfried K, Keane Pearse A, Wong Tien Yin, Dai Rongping, Sheng Bin
Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China; Institute for Proactive Healthcare, Shanghai Jiao Tong University, Shanghai, China.
Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China; Beijing Key Laboratory of Fundus Diseases Intelligent Diagnosis & Drug/Device Development and Translation, Beijing, China.
Cell Rep Med. 2025 Jul 15;6(7):102203. doi: 10.1016/j.xcrm.2025.102203. Epub 2025 Jun 25.
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.
系统性红斑狼疮（SLE）是一种主要影响女性的严重自身免疫性疾病。然而，由于诊断标准复杂且公众认知不足，全球范围内对SLE及其相关并发症的筛查面临重大挑战。由于SLE相关的视网膜病变可以为疾病活动和严重程度提供线索，我们开发了一种深度学习系统（DeepSLE），用于从视网膜图像中检测SLE及其视网膜和肾脏并发症。在包含来自中国和英国的247,718张图像的多民族验证数据集中，DeepSLE对SLE实现了受试者操作特征曲线下面积为0.822 - 0.969。此外，DeepSLE在按性别、年龄、种族和社会经济地位分层的亚组中表现出稳健的性能。为确保DeepSLE的可解释性，我们进行了定性和定量分析。此外，在一项前瞻性读者研究中，DeepSLE与初级保健医生相比表现出更高的敏感性。总之，DeepSLE为从视网膜图像中检测SLE及其相关并发症提供了数字解决方案，具有未来临床应用的潜力。
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