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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PM developed in 64 children (17.1%). Independent predictors included lower retinal vessel density (OR, 9.69; 95%\u2009CI 4.33 to 21.70), reduced fractal dimension (OR, 5.94; 95%\u2009CI 3.07 to 11.50), narrower arteriolar and venular calibres (central retinal arteriolar equivalent: OR, 3.44; 95%\u2009CI 1.86 to 6.34; central retinal venular equivalent: OR, 4.42; 95%\u2009CI 2.24 to 8.71), thinner subfoveal choroid (OR, 2.64; 95%\u2009CI 1.56 to 4.45), faster early axial elongation (OR, 1.72; 95%\u2009CI 1.17 to 2.50) and accelerated early choroidal thinning (OR, 2.33; 95%\u2009CI 1.41 to 3.84). The AI oculomic model achieved excellent discrimination (AUROC, 0.96; 95%\u2009CI 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2026-329611","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:20:15Z","timestamp":1780593615000},"page":"bjo-2026-329611","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["AI-based oculomics for trajectory-driven risk stratification of pathologic myopia in paediatric high myopia"],"prefix":"10.1136","clinical-trial-number":[{"clinical-trial-number":"isrctn56368396","registry":"10.18810\/isrctn"}],"author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0268-9563","authenticated-orcid":false,"given":"Ziyu","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 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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"},{"name":"Guangdong Basic Research Center of Excellence for Major Blinding Eye Diseases Prevention and Treatment, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoying","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China"},{"name":"Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Hong Kong, China"},{"name":"Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sixi","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun 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