Development and validation of an AI-enhanced prediction model for 3-year visual decline in patients with diabetes using ophthalmic imaging: protocol for a real-world longitudinal cohort study.

backgroundDiabetic retinopathy (DR) is one of the leading causes of visual impairment among working-age adults worldwide. Existing artificial intelligence (AI) studies have mainly focused on automated diagnosis and grading of DR or diabetic macular oedema while relatively few studies have investigated long-term prediction of visual outcomes. In clinical practice, patients and clinicians are more concerned about whether future visual decline will occur rather than merely identifying the current presence of DR. This study aims to develop and validate a real-world prognostic model integrating AI-derived imaging scores, ophthalmic examinations and systemic clinical variables to predict 3-year visual decline in patients with diabetes. METHODS AND ANALYSIS: This retrospective longitudinal cohort study will use routinely collected ophthalmic imaging and clinical data from Peking University Third Hospital for model development and internal validation, with independent external validation planned using data from Ningbo Eye Hospital, Wenzhou Medical University. The development cohort will cover the period from 1 January 2018 to 1 May 2026. Eligible eyes will have baseline colour fundus photography (CFP) and/or optical coherence tomography (OCT), baseline best-corrected visual acuity and at least one follow-up visual acuity record within 3 years after the index date. CFP-enhanced analyses will include eyes with eligible CFP, OCT-enhanced analyses will include eyes with eligible OCT and multimodal analyses will require eligible paired CFP and OCT. The primary outcome will be time to first visual decline within 3 years, defined as an increase in logarithm of the minimum angle of resolution (logMAR) best-corrected visual acuity of ≥0.2 from baseline in the same eye. Sustained visual decline, requiring confirmation of a ≥0.2-logMAR deterioration at a subsequent eligible visit, will be evaluated as a key secondary outcome. AI-derived imaging scores will be generated using prespecified modality-specific and multimodal retinal image models. Separate CFP and OCT models will generate continuous CFP-derived and OCT-derived imaging scores, while a prespecified multimodal model jointly integrating paired CFP and OCT information will generate a single continuous multimodal imaging score. These imaging scores will be incorporated separately into prespecified AI-enhanced prognostic models together with demographic, systemic and ophthalmic predictors. Cox regression, penalised Cox regression and exploratory machine learning survival models will be developed and internally validated and the final prognostic model will be externally evaluated in an independent cohort from Ningbo Eye Hospital. Performance will be evaluated using discrimination, calibration, Brier score and decision curve analysis. ETHICS AND DISSEMINATION: This study has received ethical approval from the Ethics Committee of Peking University Third Hospital (approval number: IRB00006761-M20260399). All data will be de-identified before analysis. The findings of this study will be disseminated through peer-reviewed publications and conferences.