医学
视网膜
眼科
脉络膜
接收机工作特性
前瞻性队列研究
眼底(子宫)
光学相干层析成像
风险因素
视网膜
检眼镜
试验预测值
视网膜病变
眼病
纵向研究
视神经
曲线下面积
危险分层
糖尿病性视网膜病变
神经纤维层
外科
青光眼
临床实习
验光服务
荧光血管造影
作者
Ziyu Zhu,Hui Li,Ruilin Xiong,Shaoying Tan,Sixi Zeng,Shida Chen,Mingguang He,Wei Wang
标识
DOI:10.1136/bjo-2026-329611
摘要
AIMS: To identify early oculomic biomarkers predictive of pathologic myopia (PM) in children with high myopia (HM) and to develop an artificial intelligence (AI)-based model for individualised risk stratification. METHODS: This prospective longitudinal study included 375 children with bilateral HM (spherical equivalent ≤ -6.00 dioptres (D)) from the Zhongshan High Myopia Cohort, followed for a median of 15.0 years (IQR, 14.9-15.4; range, 14.7-15.7). Deep learning-based automated retinal vascular phenotyping was applied to fundus photographs and integrated with longitudinal ocular biometry and swept-source optical coherence tomography metrics. Multivariable models identified independent predictors of PM onset and predictive performance was assessed using area under the receiver operating characteristic curve (AUROC). A web-based prediction tool was developed for clinical deployment. RESULTS: PM developed in 64 children (17.1%). Independent predictors included lower retinal vessel density (OR, 9.69; 95% CI 4.33 to 21.70), reduced fractal dimension (OR, 5.94; 95% CI 3.07 to 11.50), narrower arteriolar and venular calibres (central retinal arteriolar equivalent: OR, 3.44; 95% CI 1.86 to 6.34; central retinal venular equivalent: OR, 4.42; 95% CI 2.24 to 8.71), thinner subfoveal choroid (OR, 2.64; 95% CI 1.56 to 4.45), faster early axial elongation (OR, 1.72; 95% CI 1.17 to 2.50) and accelerated early choroidal thinning (OR, 2.33; 95% CI 1.41 to 3.84). The AI oculomic model achieved excellent discrimination (AUROC, 0.96; 95% CI 0.92 to 1.00), improving to 0.98 (95% CI 0.95 to 1.00) with biometric variables. These predictors were implemented in a publicly accessible risk prediction platform (System for Myopia AI-based Risk Tracking-PM). CONCLUSIONS: PM risk in paediatric HM is detectable early through retinal microvascular architecture and ocular growth dynamics. AI-enabled oculomic profiling offers a scalable approach to early risk stratification, supporting targeted surveillance and timely prevention of sight-threatening pathology.
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