An Interpretable Fundus Diseases Report Generating System Based On Weakly Labelings

To establish a multimodal fundus image report generation model to realize an interpretable system for multiple fundus diseases, multimodal image analysis, diagnosis, and treatment decision automatic reporting based on weakly labeled training data. Construct an interpretable feature fusion network for the clinical and imaging features of fundus lesions, and we hope to extract new imaging markers that can predict the occurrence and progression of various fundus lesions at an early stage, and ultimately verify them in real clinical data, further providing possible directions for exploring the molecular mechanisms of refractory fundus lesions, and may also provide new ideas for the precise prevention and treatment of fundus lesions.

NCT Number	NCT06918028
Lead Sponsor	Zhongshan Ophthalmic Center, Sun Yat-sen University
Conditions	Retinal Diseases, Choroidal Disease, Pathological Myopia, Diabetic Retinopathy, Age Related Macular Degeneration, Choroidal Neovascularization
Enrollment	9,999 participants
Start Date	2025-05
Primary Completion	2025-12 (estimated)
Study Completion	2026-12 (estimated)
Updated on ClinicalTrials.gov	2025-04-09