Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications: LC-OCT reveals new biological markers of AD.

objectiveThis study explores the application of Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging coupled with artificial intelligence (AI)-based algorithms to investigate atopic dermatitis (AD), a common inflammatory dermatosis. MATERIALS AND

methodsAD acute and chronic lesions (ADL) were compared to clinically healthy-looking skin (ADNL). LC-OCT was used noninvasively and in real-time to image the skin of AD patients during flare-ups and monitor remissions under topical steroid treatment for 2 weeks. Quantitative parameters were extracted from the images, including morphological and cellular-level markers of epidermal architecture. A novel cellular-level parameter, nuclei "atypia," which quantifies the orderliness of epidermal renewal, was used to highlight abnormal maturation processes.

resultsCompared to healthy skin, AD lesions exhibited significant increases in both epidermal and stratum corneum (SC) thickness, along with a more undulated dermo-epidermal junction (DEJ). Additionally, keratinocyte nuclei (KN) were larger, less compact, and less organized in lesional areas, as indicated by the atypia parameter. A higher degree of atypia was observed in chronic lesions compared to acute ones. Following treatment, all the parameters normalized to levels observed in healthy skin within 2 weeks, mirroring clinical improvements.

conclusionThis study provides insights into the quantification of epidermal renewal using a noninvasive imaging technique, highlighting differences between ADL/ADNL and acute/chronic lesions. It also presents the AD treatment mechanism, paving the way for future investigations on AD and other skin barrier function-related conditions.