Cell子刊：盛斌/戴荣平团队开发新型AI模型DeepSLE，从视网膜图像检测系统性红斑狼疮

Core Viewpoint - The article discusses the development of a deep learning system called DeepSLE for detecting systemic lupus erythematosus (SLE) from retinal images, highlighting its potential to improve early diagnosis and management of the disease and its complications [4][5][12]. Group 1: Disease Overview - Systemic lupus erythematosus (SLE) is a severe autoimmune disease affecting approximately 3.4 million people globally, with an estimated 3 million being women [2]. - The likelihood of women developing SLE is several times higher than that of men, with a peak incidence typically occurring between the ages of 15 and 45 [2]. Group 2: Screening Challenges - There is a significant challenge in the early detection of SLE due to the lack of widely accepted, standardized, non-invasive, and cost-effective screening tools, especially for asymptomatic or mildly symptomatic individuals [3]. - Current screening methods for SLE-related complications, such as lupus retinopathy (LR) and lupus nephritis (LN), are not routinely implemented in primary care settings, particularly in resource-limited environments [7]. Group 3: DeepSLE Development - The DeepSLE system was developed using a dataset of 666,383 retinal images from 173,346 participants for pre-training, followed by training and validation on over 254,246 images from 91,598 participants across diverse ethnic backgrounds [9]. - The system demonstrated a robust performance in detecting SLE, achieving an area under the receiver operating characteristic curve (AUC) ranging from 0.822 to 0.969 in a multi-ethnic validation dataset [11]. Group 4: Clinical Implications - DeepSLE offers a digital solution for detecting SLE and its related complications from retinal images, presenting significant clinical application potential [12]. - The system showed higher sensitivity compared to primary care physicians in a prospective reader study, indicating its effectiveness in clinical settings [11].