Diagnostic challenges in high myopia: identification of sight-threatening complications and the role of artificial...

High myopia (HM), defined as a spherical equivalent refractive error ≤ -5.00 or ≤ -6.00 diopters or axial length (AL) ≥ 26.0 mm, is a significant public health concern with a rapidly increasing prevalence, particularly in East Asia. Beyond impaired uncorrected vision, HM is associated with sight-threatening structural changes, including myopic maculopathy, choroidal neovascularization, retinal detachment, and glaucoma. The overlapping and atypical presentations of these complications pose considerable diagnostic challenges, often delaying intervention and complicating clinical management. This review synthesizes current knowledge on HM, emphasizing the spectrum of ocular complications and the multifaceted diagnostic dilemmas encountered. We have summarized the application of conventional and emerging diagnostic techniques-such as optical coherence tomography (OCT), ultra-widefield imaging, and fluorescein angiography in the diagnosis of high myopia and highlight the growing role of artificial intelligence (AI) and machine learning in enhancing diagnostic accuracy, particularly through the analysis of retinal images and OCT data. AI-based systems demonstrate high sensitivity and specificity in detecting HM-related pathologies, offering potential for large-scale screening and early intervention. Future directions include the development of integrated multimodal imaging platforms, genetic and metabolic biomarkers, and AI-driven predictive models to support personalized management strategies. This comprehensive overview underscores the need for advanced, accessible diagnostic tools to alleviate the burden associated with high myopia.