Toward dynamic tongue diagnosis: a conceptual framework for temporal phenotyping.

Digital tongue diagnosis has advanced considerably through image-based feature extraction and machine learning, yet its dominant analytical paradigm remains anchored in static, single-frame examination-an approach that inherently discards temporal information generated during the examination process, including stabilization dynamics, fluctuation patterns, and short-term physiological responsiveness. In this Perspective, we argue for a reorientation from static feature categorization towards temporal phenotyping. We propose a four-component analytical framework comprising: (i) standardized video acquisition with minimum technical specifications for clinical feasibility; (ii) temporal event alignment anchored to physiologically defined reference points such as protrusion onset and maximal extension; (iii) steady-state window identification using CIELAB ΔE-based criteria to isolate analytically meaningful intervals; and (iv) dynamic feature representation that distinguishes quantitative temporal metrics from clinically interpretable phenotypes. We further delineate the evidentiary requirements-biological plausibility, measurement reproducibility, analytical validity, and clinical utility-necessary to substantiate this paradigm shift, and outline a staged validation pathway from controlled laboratory studies to prospective clinical evaluation. Potential applications include longitudinal health monitoring, individualised assessment, and telemedicine integration. Despite substantial translational barriers, we contend that a temporally informed framework offers a more comprehensive and clinically meaningful foundation for next-generation digital tongue diagnosis systems.