Dermatologie (Heidelberg, Germany) · 2025
[Line-field confocal optical coherence tomography and artificial intelligence].
Аннотация на английскомСтатьяМетаанализНазвание и аннотация: Перевод в очереди
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Mayer Oliver, Wirsching Hanna, Thamm Janis, Welzel Julia, Schuh Sandra
Аннотация
BACKGROUND: The diagnosis of actinic keratosis (AK), basal cell carcinoma (BCC), and psoriasis may present a challenge in everyday dermatological practice. Clinical and dermoscopic assessments often reach their limits, especially in ambiguous or anatomically difficult-to-access lesions. Biopsies are often impractical, and objective tools for treatment monitoring are lacking.
OBJECTIVE: To investigate the potential of line-field confocal optical coherence tomography (LC-OCT) combined with artificial intelligence (AI) for noninvasive diagnosis, differentiation, and longitudinal monitoring.
MATERIALS AND METHODS: Analysis and evaluation of LC-OCT imaging data from various studies. Application of AI-based algorithms for the detection of vascular patterns, epidermal changes, and BCC identification using heatmap-supported decision tools.
RESULTS AND DISCUSSION: The LC-OCT enables high-resolution, real-time visualization of dermoepidermal structures as well as vascular architecture. In combination with AI, objective parameters such as PRO score, atypia, and vascular morphology can be quantified and monitored over time. AI-assisted diagnostics significantly improve diagnostic accuracy-especially in BCC and among less experienced users. However, implementation requires clear guidelines, standardization, and well-defined legal and ethical frameworks.
CONCLUSION: The LC-OCT combined with AI is a promising tool for more precise, standardized, and personalized dermatological diagnostics. Particularly in AK, BCC, and psoriasis, it has the potential to enhance care, reduce the need for invasive procedures, and provide novel insights into tumor and inflammation biology.
OBJECTIVE: To investigate the potential of line-field confocal optical coherence tomography (LC-OCT) combined with artificial intelligence (AI) for noninvasive diagnosis, differentiation, and longitudinal monitoring.
MATERIALS AND METHODS: Analysis and evaluation of LC-OCT imaging data from various studies. Application of AI-based algorithms for the detection of vascular patterns, epidermal changes, and BCC identification using heatmap-supported decision tools.
RESULTS AND DISCUSSION: The LC-OCT enables high-resolution, real-time visualization of dermoepidermal structures as well as vascular architecture. In combination with AI, objective parameters such as PRO score, atypia, and vascular morphology can be quantified and monitored over time. AI-assisted diagnostics significantly improve diagnostic accuracy-especially in BCC and among less experienced users. However, implementation requires clear guidelines, standardization, and well-defined legal and ethical frameworks.
CONCLUSION: The LC-OCT combined with AI is a promising tool for more precise, standardized, and personalized dermatological diagnostics. Particularly in AK, BCC, and psoriasis, it has the potential to enhance care, reduce the need for invasive procedures, and provide novel insights into tumor and inflammation biology.