Related Experiment Video
Updated: Jun 6, 2026
Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
AI-based oculomics for trajectory-driven risk stratification of pathologic myopia in paediatric high myopia
Ziyu Zhu1,2, Huangdong Li1,2, Ruilin Xiong1,2
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
Insights
Early detection of pathologic myopia (PM) in children with high myopia (HM) is possible using retinal imaging and AI. This approach enables personalized risk assessment to prevent vision loss.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- High myopia (HM) in children poses a significant risk for developing pathologic myopia (PM), a leading cause of vision impairment.
- Early identification of children at risk for PM is crucial for timely intervention and prevention of sight-threatening complications.
Purpose of the Study:
- To identify early oculomic biomarkers that predict the onset of pathologic myopia (PM) in children with high myopia (HM).
- To develop an artificial intelligence (AI)-based model for individualized risk stratification of PM in pediatric HM patients.
Main Methods:
- A prospective longitudinal study of 375 children with HM followed for a median of 15 years.
- Deep learning-based retinal vascular phenotyping combined with ocular biometry and OCT metrics.
- Multivariable models and AI were used to identify predictors and assess predictive performance (AUROC).
Main Results:
- Pathologic myopia (PM) developed in 17.1% of children.
- Key predictors included reduced retinal vessel density, fractal dimension, narrower arterioles/venules, thinner choroid, faster axial elongation, and accelerated choroidal thinning.
- The AI oculomic model demonstrated excellent predictive discrimination (AUROC 0.96-0.98).
Conclusions:
- Early signs of pathologic myopia (PM) risk in pediatric high myopia (HM) can be detected through retinal microvascular changes and ocular growth patterns.
- AI-powered oculomic profiling provides a scalable method for early risk stratification.
- This approach supports targeted surveillance and timely prevention strategies for sight-threatening conditions.
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.
Related Concept Videos
01:25Glaucoma: Overview
01:27Open Angle Glaucoma: Treatment
Drugs such as carbonic anhydrase inhibitors, α2- and...