Authors
Huixun Jia, Bo Qian, Yanlin Qu, Jinyuan Wang, Jieqiong Chen, Tong Li, Changle Zheng, Jinu Han, Guanran Zhang, Yixiao Jin, Seungmin Lee, Xiang Chen, Haichao Chen, Jiayu Xu, Ke Xu, Yu Rong, Yi Jiang, Yiliu Yang, Wenjing Li, Qingge Guo, Ya Li, Yang Li, Jihong Wu, Qingjiong Zhang, Fang Lu, Wenjuan Zhuang, Bo Lei, Suk Ho Byeon, Junwon Lee, Adrian Smedowski, Marta Swierczynska, Daoqiang Zhang, Tyler Hyungtaek Rim, Andrzej Grzybowski, Bin Sheng, Tien Yin Wong, Xiaodong Sun
Published in
Nature medicine. Jul 24, 2026. Epub Jul 24, 2026.
Abstract
The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using multimodal data with color fundus photographs and optical coherence tomography scans from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland. The top-5 prediction accuracy was 0.904 (95% confidence interval (CI): 0.896-0.912) and 0.856 (95% CI: 0.850-0.863) for internal and external validation, respectively. We conducted a randomized controlled trial with 300 participants with suspected IRD randomized 1:1 to either Retina4IRD-assisted specialist arm or specialist-only arm. Of these, 295 participants (median age 33 years, 114 (38.6%) females) with available next-generation sequencing reports were included in the final analysis. The primary outcome was met: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001). For secondary endpoints, top-1 to top-4 accuracies all favored the Retina4IRD-assisted specialist arm, with top-1 accuracy of 37.8% versus 22.4% and top-4 accuracy of 81.8% versus 53.1%, respectively. Post hoc analyses demonstrated that, with Retina4IRD assistance, clinicians made better management decisions, and the composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001). Our study shows that Retina4IRD is a CDSS tool prior to genetic testing and aligns with clinical workflow for patients with suspected IRDs. ClinicalTrials.gov identifier: NCT06839170 .
PMID:
42498742
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.
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