Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning.

Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years.

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.

Background: Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years.

Materials And Methods: In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible.

Results: Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups.