2025/04/10 by Sam Kahler, Chantal Rutjes, Monika Janda +2 · 1 voice
Medicine · #Cutaneous Melanoma Detection and Management
paper · pdf · doi:10.1553/skindeep.2025.150261
openalex publication_date 2025/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/07
Targeted surveillance for individuals at high-risk for melanoma is increasingly recognized as a feasible and effective alternative to population-wide melanoma screening. However, current risk stratification models to identify these high-risk individuals are often reliant on subjective or self-reported metrics, which are vulnerable to bias and poor reproducibility. The deep imaging phenotype describes the concept of leveraging parallel advances in total body photography (TBP) and artificial intelligence (AI) to improve risk stratification using personalized severity and spatial distribution of cutaneous risk factors. This narrative review explores the progress towards the deep imaging phenotype in dermatology, with a focus on its clinical applications, challenges, and future directions. It explores (i) the limitations of existing melanoma risk prediction, (ii) advancements in TBP and AI-driven analysis of the cutaneous phenotype; (iii) integration of phenotype with clinical and genomic information; (iv) frameworks for clinical, logistic, and ethical implementation of phenotypic measures into clinical practice. Progress towards the deep imaging phenotype has included algorithms that report nevus characteristics (count, size, and distribution), severity and distribution of photodamage, facultative and innate skin tones, freckling, and other parameters for objective and personalized risk stratification. Phenotypic measures correlate with melanoma risk and may be integrated with traditional clinical and genomic risk factors to enhance current risk assessment. Clinicians and consumers report acceptance of this approach, although, most evidence to date focuses on individual phenotypic features rather than the collective synergy of all measures. Supporting information technology infrastructure, legal frameworks, and clinical guidelines are underdeveloped and should be prioritized before clinical implementation. Objective risk stratification using personalized cutaneous risk factors may empower the effective allocation of resources, reduce over-surveillance in low-risk populations, and offer timely interventions to individuals at high risk.