Assessment of dermal fiber changes associated with age ethnicity and cosmetic product use by LC-OCT and automated 3D segmentation.

This work presents a novel, non-invasive method that combines high-resolution 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) images with an advanced, in-house developed automated 3D segmentation algorithm to quantitatively analyze dermal fiber characteristics in vivo. This approach marks the first in-depth investigation of dermal fibers, enabling precise characterization of age-related changes, ethnic differences, and the effects of anti-aging skincare products on the cheekbone region of Caucasian and Asian women. Our algorithm accurately extracts fiber metrics, revealing that aging correlates with shorter fiber length and increased anisotropy. Although Asians exhibited a denser fiber network than Caucasians, both ethnicities showed comparable mean fiber lengths and anisotropy. Furthermore, anti-aging skincare treatments significantly enhanced fiber length, node count, and network density while reducing anisotropy over one and three months. This innovative integration of cutting-edge imaging and algorithmic analysis provides valuable insights for cosmetic applications and paves the way for future non-invasive dermatological research.