首页|Saint-Louis Hospital Reports Findings in Artificial Intelligence (Epidermal rene wal during the treatment of atopic dermatitis lesions: A study coupling line-fie ld confocal optical coherence tomography with artificial intelligence ...)
Saint-Louis Hospital Reports Findings in Artificial Intelligence (Epidermal rene wal during the treatment of atopic dermatitis lesions: A study coupling line-fie ld confocal optical coherence tomography with artificial intelligence ...)
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New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Paris , France, by NewsRx correspondents, research stated, "This study explores the ap plication of Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging c oupled with artificial intelligence (AI)-based algorithms to investigate atopic dermatitis (AD), a common inflammatory dermatosis. AD acute and chronic lesions (ADL) were compared to clinically healthy-looking skin (ADNL)." Our news editors obtained a quote from the research from Saint-Louis Hospital, " 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 we eks. Quantitative parameters were extracted from the images, including morpholog ical and cellular-level markers of epidermal architecture. A novel cellular-leve l parameter, nuclei ‘atypia,' which quantifies the orderliness of epidermal rene wal, was used to highlight abnormal maturation processes. Compared to healthy sk in, AD lesions exhibited significant increases in both epidermal and stratum cor neum (SC) thickness, along with a more undulated dermo-epidermal junction (DEJ). Additionally, keratinocyte nuclei (KN) were larger, less compact, and less orga nized in lesional areas, as indicated by the atypia parameter. A higher degree o f atypia was observed in chronic lesions compared to acute ones. Following treat ment, all the parameters normalized to levels observed in healthy skin within 2 weeks, mirroring clinical improvements. This study provides insights into the qu antification of epidermal renewal using a noninvasive imaging technique, highlig hting differences between ADL/ADNL and acute/chronic lesions."
ParisFranceEuropeArtificial Intell igenceAtopic DermatitisBiological FactorsBiomarkersDermatitisDermatolo gyDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineImagi ng TechnologyMachine LearningOptical Coherence TomographySkin Diseases and ConditionsSkin and Connective Tissue Diseases and ConditionsTechnology
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