Interpreting ocular age estimates: source, output, evidence, and clinical meaning

Ocular age estimates derived from fundus photographs, optical coherence tomography, ocular fluids, systemic omics, and multimodal models are increasingly reported in years. The eye is unusually tractable for organ-specific aging research because it is paired, repeatedly imageable, locally treatable, and linked to quantitative function. The familiar scale can obscure differences in what was measured, what the model was developed to predict, which quantity is reported, and what the study design can support. Existing frameworks classify visual-aging biomarkers by biological domain and context of use. We propose an eye-specific framework organized around five declarations: proximal source; development target; reported quantity; evidence design; and intended use. Laterality, reference population, disease and treatment context, technical version, and uncertainty in the stated output remain essential annotations. A raw age gap is a cross-sectional residual, and an age-adjusted deviation requires calibration outside the outcome-analysis sample. Repeated application of a cross-sectional model does not establish an individual aging rate; longitudinal claims require repeated measurements against a longitudinal reference and change distinguishable from measurement variability. Visual-aging claims also need psychophysical, performance-based, or patient-reported validation. Differences across measurement domains must be assessed both against measurement error and against the distribution expected in the reference population. A persistent difference may support a candidate biological pattern only after technical and clinical explanations have been evaluated. Prognostic and clinical claims require separate prospective and decision-level evidence. These distinctions prevent a cross-sectional age residual from being mistaken for an aging rate, a prognostic marker, or a clinically actionable test.