| Title: | A deep learning system for detecting systemic lupus erythematosus from retinal images |
| Journal: | Cell Reports Medicine |
| Published: | 25 Jun 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40570853/ |
| DOI: | https://doi.org/10.1016/j.xcrm.2025.102203 |
| URL: | https://www.cell.com/action/showPdf?pii=S2666379125002769 |
Publication 16405
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
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.</p>
10 Keywords
- Adult
- Deep Learning
- Female
- Humans
- Lupus Erythematosus, Systemic
- Male
- Middle Aged
- ROC Curve
- Retina
- Young Adult
31 Authors
- Tingyao Li
- Shiqun Lin
- Zhouyu Guan
- Yukun Zhou
- Dian Zeng
- Zheyuan Wang
- Yan Zhou
- Pinqi Fang
- Shujie Yu
- Ruhan Liu
- Xiang Chen
- Yan-Ran Wang
- Yuwei Lu
- Jia Shu
- Yiming Qin
- Yiting Wu
- Yilan Wu
- Chan Wu
- Shangzhu Zhang
- Jie Shen
- Huating Li
- Tingli Chen
- Jin Li
- Yih-Chung Tham
- Charumathi Sabanayagam
- Ying Feng Zheng
- Siegfried K. Wagner
- Pearse A. Keane
- Tien Yin Wong
- Rongping Dai
- Bin Sheng
1 Application
| Application ID | Title |
|---|---|
| 104443 | The artificial intelligence supported system in the diagnosis and risk prediction of metabolic disorders based on multimodal data |
Enabling scientific discoveries that improve human health