ArticleJournal of the European Academy of Dermatology and Venereology : JEADV2026
AI-assisted basal cell carcinoma diagnosis with LC-OCT: A multicentric retrospective study.
Article in Journal of the European Academy of Dermatology and Venereology : JEADV, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Emerging In Vivo and Ex Vivo Optical Imaging Technologies in Dermatology and Dermatopathology.Dermatopathology (Basel, Switzerland) · 2026Review
- Article
- Digital Dermatology.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2026Review
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- Non-invasive characterization of the relationship between skin microrelief and dermal-epidermal junction topography using line-field confocal optical coherence tomography (LC-OCT).Scientific reports · 2026Article
- AI-assisted basal cell carcinoma diagnosis with LC-OCT: A multicentric retrospective study.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Article
- Intraoperative Margin Control in Eyelid Tumor Surgery: Current Standards, Imaging Advances, and Emerging Techniques.Current oncology (Toronto, Ont.) · 2026Review
- Co-Localized Dermoscopy and LC-OCT for AI-Assisted Margin Assessment of Basal Cell Carcinoma: Development of a "BCC-One-Stop-Shop" Workflow.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic Performance of Line-Field Confocal Optical Coherence Tomography for Basal Cell Carcinoma: A Prospective Study.Cancers · 2026Article
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
- Line-Field Confocal Optical Coherence Tomography of Basal Cell Carcinoma: Systematic Correlation with Histopathology.Diagnostics (Basel, Switzerland) · 2025Article
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22 authors.
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Abstract
backgroundBasal cell carcinoma (BCC) is the most common skin cancer, requiring an early diagnosis and accurate margin definition to prevent functional and cosmetic complications. Traditional methods using clinical and dermoscopic images (C&D) often rely on biopsies and histology for final validation. Non-invasive techniques like LC-OCT, enabling 'digital biopsies', are promising alternatives, but remain underutilized due to the expertise required. The development of Artificial Intelligence (AI) algorithms is a promising approach to assist dermatologists in their diagnosis and support the broader adoption of such technologies.
objectivesWe present a real-time AI assistant for BCC diagnosis with LC-OCT, which is, to date, the only real-time AI model across all dermatological imaging modalities. The study aims to quantify the model's effectiveness when used by dermatologists with different levels of expertise and compare its performance with traditional methods and unaided LC-OCT.
methodsThis multicenter, retrospective study involved 43 dermatologists in a double-rounded quiz on 200 equivocal BCC lesions. Diagnoses were first made on C&D images, then with LC-OCT or AI-assisted LC-OCT in a randomized manner.
resultsAI-assisted LC-OCT significantly improves dermatologists' diagnostic performance in detecting BCC (+25.8 points in sensitivity and +16.8 points in specificity compared to C&D), particularly benefiting those with less LC-OCT experience, effectively bridging a 2-year gap of expertise. These results highlight the potential for broader clinical adoption through AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.
conclusionsThese results support a broader adoption of LC-OCT use in clinical practice thanks to AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.
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