AI framework for multidisease detection via retinal imaging

The rising burden of endocrine and metabolic diseases demands scalable and accessible screening tools. Here we developed Reti-Pioneer, a multitask retinal imaging framework that integrates quality-aware modules with pre-trained foundation models for efficient, multidisease detection. In general, the framework was developed using 107,730 color fundus photographs from both community-based and hospital-based cohorts and achieved area under the receiver operating characteristic curve values on internal test data of 0.833 (95% confidence interval 0.810-0.856) for type 2 diabetes mellitus, 0.832 (0.799-0.866) for gout, 0.787 (0.742-0.833) for osteoporosis, 0.740 (0.726-0.755) for hypertension, 0.736 (0.721-0.751) for hyperlipidemia and 0.699 (0.667-0.730) for thyroid disease. The framework generalized well to six external cohorts from both resource-limited and high-resource settings, and showed biological interpretability via plasma proteomic correlations. In a primary care silent trial, it completed screening in 30.6 ± 6.0 s per case, notably faster than standard laboratory workflows. A subsequent clinical pilot for type 2 diabetes mellitus yielded an area under the receiver operating characteristic curve of 0.776 (0.710-0.842) and negative predictive value of 0.966 (0.946-0.983), surpassing the Finnish Diabetes Risk Score, with high acceptance from clinicians and patients. Overall, Reti-Pioneer could provide a translatable, low-cost pathway from oculomics to actionable clinical screening.

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Publication Details

Journal
Nature Medicine
Published
2026-04-28
DOI
https://doi.org/10.1038/s41591-026-04359-w
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

AI framework for multidisease detection via retinal imaging

Zhuoting Zhu, Yangqin Feng, Chunran Lai, Kaiyi Chi et al.
Nature Medicine
Retinal Imaging and Analysis
article

AI framework for multidisease detection via retinal imaging

Zhuoting Zhu, Yangqin Feng, Chunran Lai, Kaiyi Chi, Hongyang Jiang, Xi Zhang, Miao Lin, Chunxin Liu, Wing W. Y. Ng, J Cao, Qinyi Li, Hu Lumei, Ching-Yu Cheng, Yih Chung Tham, Fan Xu, Shan Wang, Wenyi Hu, Feng Zhang, Xiaohong Yang, Li Li, Bin Sheng, Yukun Zhou, Cuomu Duojie, Ying Fang, Carol Y. Cheung, Honghua Yu, Yinhao Liang
article en

Abstract

The rising burden of endocrine and metabolic diseases demands scalable and accessible screening tools. Here we developed Reti-Pioneer, a multitask retinal imaging framework that integrates quality-aware modules with pre-trained foundation models for efficient, multidisease detection. In general, the framework was developed using 107,730 color fundus photographs from both community-based and hospital-based cohorts and achieved area under the receiver operating characteristic curve values on internal test data of 0.833 (95% confidence interval 0.810-0.856) for type 2 diabetes mellitus, 0.832 (0.799-0.866) for gout, 0.787 (0.742-0.833) for osteoporosis, 0.740 (0.726-0.755) for hypertension, 0.736 (0.721-0.751) for hyperlipidemia and 0.699 (0.667-0.730) for thyroid disease. The framework generalized well to six external cohorts from both resource-limited and high-resource settings, and showed biological interpretability via plasma proteomic correlations. In a primary care silent trial, it completed screening in 30.6 ± 6.0 s per case, notably faster than standard laboratory workflows. A subsequent clinical pilot for type 2 diabetes mellitus yielded an area under the receiver operating characteristic curve of 0.776 (0.710-0.842) and negative predictive value of 0.966 (0.946-0.983), surpassing the Finnish Diabetes Risk Score, with high acceptance from clinicians and patients. Overall, Reti-Pioneer could provide a translatable, low-cost pathway from oculomics to actionable clinical screening.

Nature Medicine
Moorfields Eye Hospital NHS Foundation Trust (GB), Xinjiang Medical University (CN), Sun Yat-sen University (CN), Guangxi Medical University (CN), The University of Melbourne (AU), National University of Singapore (SG), Chinese University of Hong Kong (HK), Shanghai Jiao Tong University (CN), Singapore National Eye Center (SG), First Affiliated Hospital of Xinjiang Medical University (CN), Linyi People's Hospital (CN), Centre for Eye Research Australia (AU), Singapore Eye Research Institute (SG), Fuzhou Second Hospital (CN), Duke-NUS Medical School (SG), The People's Hospital of LinXia (CN), Guangdong Academy of Medical Sciences (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), Moorfields Eye Hospital (GB), Guangdong Provincial People's Hospital (CN), University College London (GB), Southern Medical University (CN), Victoria University (AU), Fuzhou University (CN), South China University of Technology (CN)
Openalex Percentile: Top 8%
Retinal Imaging and Analysis
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