Emerging non-invasive tools for hair follicle assessment: a narrative review on hyperspectral and terahertz imaging.

Hair loss, though often dismissed as cosmetic, reflects complex vascular, metabolic, structural, and inflammatory changes within the scalp microenvironment. Conventional diagnostic tools such as visual inspection, dermoscopy, trichoscopy, and biopsy remain limited in their ability to determine these evolving processes non-invasively. Recent advances in optical physics and biomedical engineering have introduced light- and wave-based imaging approaches that may expand scalp diagnostics beyond surface visualization. Hyperspectral imaging (HSI) and terahertz (THz) imaging have demonstrated comparable capabilities for assessing tissue biochemistry, microstructure, hydration dynamics, pigmentation, and vascular signatures in related dermatologic applications and may support exploratory investigation of scalp assessment after validation. However, direct clinical evidence supporting HSI and THz imaging specifically in hair-loss patients remains limited. Therefore, this review integrates available alopecia-related imaging evidence, mechanistic and modeling evidence relevant to hair biology, and extrapolated findings from broader dermatologic applications, including oncology and wound-healing research. Relevant literature was identified by structured searches of PubMed, Scopus, and Google Scholar, covering studies published from June 2008 to March 2026. The review analyzes how these technologies may identify indirect, non-invasive scalp-tissue indicators associated with follicular miniaturization, perifollicular inflammation, fibrosis, vascular compromise, hydration imbalance, and treatment response. By merging spectral and wave-derived data with AI-based analytics and quantitative biomarkers, HSI and THz imaging may, upon validation, contribute to a shift from subjective grading to dynamic exploratory physiological mapping of superficial scalp tissue. Nevertheless, clinical translation will require scalp-specific validation, standardized acquisition protocols, normative datasets among different hair types, and correlation with established trichoscopic, histological, and clinical endpoints. Overall, these technologies constitute promising but investigational frontiers in precision alopecia diagnostics and data-guided hair-loss management.