Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms to identify quantitative biomarkers of facial skin ageing

Abstract

Quantitative biomarkers of facial skin ageing were studied from one hundred healthy Caucasian female volunteers, aged 20-70 years, using in vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging coupled with Artificial Intelligence (AI)-based quantification algorithms. Layer metrics, i.e. stratum corneum thickness (SC), viable epidermal thickness and Dermal-Epidermal Junction (DEJ) undulation, as well as cellular metrics were measured for the temple, cheekbone and mandible. For all three investigated facial areas, minimal age-related variations were observed in the thickness of the SC and viable epidermis layers. A flatter and more homogeneous epidermis (decrease in the standard deviation of the number of layers means), a less dense cellular network with fewer cells per layer (decrease in cell surface density), and larger and more heterogeneous nuclei within each layer (increase in nuclei volume and their standard deviation) were found with significant variations with age. The higher atypia scores further reflected the heterogeneity of nuclei throughout the viable epidermis. The 3D visualisation of fine structures in the skin at the micrometric resolution and the 1200 µm × 500 µm field of view achieved with LC-OCT imaging enabled to compute relevant quantitative biomarkers for a better understanding of skin biology and the ageing process in vivo.