ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

ABSTRACT Age‐related macular degeneration (AMD) is an ordered, bilateral, and longitudinal disease, yet many artificial intelligence systems treat it as static binary image classification. We developed ORBIT‐AMD, a multimodal trajectory‐learning framework integrating color fundus photography and optical coherence tomography, bilateral eye‐graph attention, concept bottlenecks, ordinal staging, cause‐specific discrete‐time survival prediction, and protocol alignment. In a UK Biobank development/internal‐testing cohort of 58 214 participants and 109 691 eyes, and an external Tianjin Medical University Eye Hospital cohort of 1996 participants and 3780 eyes, ORBIT‐AMD achieved AUROC values of 0.984 internally and 0.975 externally for prevalent late‐AMD detection. Five‐year late‐AMD progression prediction achieved AUROC values of 0.825 and 0.767, respectively. Calibration and threshold analyses showed cohort‐dependent workload and absolute‐risk behavior, supporting site‐specific calibration assessment and clinical‐workflow evaluation before deployment. The concept bottleneck provided auditable lesion‐level explanations, but these outputs should be interpreted as structured predictive explanations rather than causal evidence. ORBIT‐AMD provides a trajectory‐aware framework for AMD risk stratification and review prioritization, with prospective validation required before clinical implementation.