Advances and challenges from pathological mechanisms to intelligent quantified diagnosis in diabetic optic neuropathy

Diabetic optic neuropathy (DON) is an increasingly recognized, distinct neurodegenerative complication of diabetes and a significant independent cause of vision loss. Its diagnosis is challenging due to heterogeneity, tool limits, and lack of biomarkers, leading to underdiagnosis and delayed intervention. This review aims to provide a comprehensive overview of DON, focusing on its pathophysiological mechanisms, current diagnostic challenges, and the emerging role of artificial intelligence (AI) as a transformative tool for enabling earlier detection and personalized management. This review provides a narrative synthesis of the literature on DON, covering clinical manifestations and multifactorial pathophysiology involving metabolic, vascular, inflammatory, and neurodegenerative pathways. Based on the foundational success in application of AI in diabetic retinopathy (DR), the translational application of machine learning and deep learning algorithms is systematically explored, covering key areas such as optic nerve head segmentation, disease classification, differential diagnosis, predictive analytics, and the discovery of novel imaging biomarkers through radiomics. AI demonstrates significant potential in quantifying subtle structural signs of DON and integrating multimodal data to overcome current diagnostic limitations. The transition from AI models in DR to those for DON represents a shift from detecting microvascular lesions to identifying neurodegenerative changes. Future directions hinge on developing explainable AI for clinical trust and leveraging longitudinal data for predictive modeling of disease progression. The integration of sophisticated AI tools into clinical practice is poised to shift the management of DON from reactive intervention to proactive, precision-based care, ultimately improving visual outcomes for the vast global diabetic population.