Xu Qiuyun, Siegel Amanda P, Smith Josee M D, Fakhoury Joseph W, Tsoukas Maria, Smith Hayden, Chen Chiu-Lan, Daveluy Steven, Mehregan Darius, Welzel Julia, Tkaczyk Eric R, Avanaki Kamran
Opt Lett. 2025 Jun 15;50(12):3947-3949. doi: 10.1364/OL.563493.
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.
光学相干断层扫描（OCT）对皮肤病变的检测具有足够的深度穿透力，但其检测效果可借助人工智能（AI）建模得到提升。AI模型构建通过比较健康组织和病变组织的图像来识别病变，但健康皮肤在不同皮肤类型和年龄之间差异很大。在此，我们选择了一个用于皮肤分析的常用参数——衰减系数，并分析了其在100名来自广泛皮肤类型（菲茨帕特里克I - V型）和年龄范围（13 - 83岁）的受试者的真皮、表皮以及皮肤暴露区和皮肤保护区中的变化情况。对于统计分析，我们报告了真皮与表皮以及阳光暴露区与阳光保护区在年龄和皮肤类型方面的比较是具有统计学显著性、不确定还是无统计学显著性，并给出了该参数在不同年龄和皮肤类型范围内的95%置信区间。使用健康图像预先分析特征的这一过程为如何在获取足够范围的图像以进行有效的AI模型构建的同时简化招募过程提供了路线图。我们期望这种类型的分析能够加速基于AI的OCT图像分析向临床的转化。
