Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications

Objective: This 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 methods: AD 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. Results: Compared 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. Conclusion: This 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.