OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologies, but its detection effectiveness can be aided by the assistance of artificial intelligence (AI) modeling. AI model-building identifies pathologies by comparing images from healthy and diseased tissues, but healthy skin can present as quite variable across skin types and ages. Here, we selected a commonly used parameter for skin analysis and attenuation coefficient and analyzed how it varied in the dermis and epidermis, and in skin-exposed and skin-protected regions, for 100 subjects from a wide range of skin types (Fitzpatrick types I-V) and ages (13-83). For the statistical analysis, we report whether comparisons of the dermis and epidermis and sun-exposed and sun-protected areas across age and skin type are statistically significant, indeterminate, or not statistically significant and present 95% confidence intervals for this parameter as it ranges across different ages and skin types. This process of pre-analyzing features using healthy images provides a roadmap for how to ease the recruitment process while acquiring a sufficient range of images for effective AI model-building. We expect this type of analysis can have the effect of accelerating translation of AI-based OCT image analysis to the clinic.