The predictive dermatome: a technical framework for spatial AI mapping of molecular skin aging

Skin aging is spatially heterogeneous. Chronological age and most systemic aging clocks provide useful population-level estimates, but they do not explain why adjacent or anatomically distinct skin regions may show different rates of dermal thinning, matrix remodeling, pigmentation change, vascular fragility, inflammation, or treatment response. We define the Predictive Dermatome as a computationally derived, anatomically localized skin region characterized by a shared or probabilistically related biological aging state and trajectory inferred from integrated molecular, structural, imaging, clinical, and exposomic data. Unlike a conventional neurological dermatome, which denotes a cutaneous territory associated predominantly with sensory innervation from a spinal nerve root, the Predictive Dermatome is not defined by nerve distribution. The proposed framework integrates spatial transcriptomics, spatial proteomics, single-cell or single-nucleus reference maps, optical coherence tomography, confocal or ultrasound imaging, histology when available, tape-strip biomarkers, extracellular matrix and glycation-related measurements, and exposomic data streams. We describe the learning task, input-output structure, spatial graph construction, modality-specific encoders, transformer-based token fusion, training objectives, regularization strategies, and missing-modality handling. Rather than requiring spatial omics or tissue sampling for every clinical patient, the framework uses transcriptomic, proteomic, histologic, and extracellular matrix measurements as discovery and validation layers from which clinically deployable surrogate models could be trained using non-invasive and minimally invasive inputs. We also outline preprocessing steps for high-dimensional spatial transcriptomic data, cross-modal image-omics registration, validation stages, uncertainty estimation, and ethical safeguards including privacy-preserving learning and bias auditing across skin phototypes. The Predictive Dermatome should therefore be viewed as a staged research architecture for precision dermatology, not as a currently validated clinical decision system. Its value lies in defining how molecular geography, imaging, and environmental exposure may eventually converge to support localized risk stratification, longitudinal monitoring, and biologically guided intervention in skin aging and regenerative dermatology.