ArticleClinical and experimental dermatology2024
Artificial intelligence algorithms and three-dimensional volumetric rendering for basal cell carcinoma detection and tumour depth assessment in reflectance confocal microscopy-optical coherence tomography images: a pilot study.
Article in Clinical and experimental dermatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Noninvasive Multimodal Imaging and Its Role in Diagnosing Skin Lesions in Dermatology: A Systematic Review and Meta-Analysis.American journal of clinical dermatology · 2025Pooled it
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The reflectance confocal microscopy (RCM)-optical coherence tomography (OCT) device has shown utility in detecting and assessing the depth of basal cell carcinoma (BCC) in vivo but is challenging for novices to interpret. Artificial intelligence (AI) applied to RCM-OCT could aid readers. We trained AI models, using OCT rasters of biopsy-confirmed BCC, to detect BCC, create three-dimensional rendering and automatically measure tumour depth. Trained AI models were applied to a separate test set containing rasters of BCC, benign lesions, and healthy skin. Blinded reader analysis and tumour depth correlation with histopathology were conducted. BCC detection improved from viewing OCT rasters only (sensitivity 73.3%, specificity 45.5%) to viewing rasters with AI-generated BCC rendering (sensitivity 86.7%, specificity 48.5%). A Pearson correlation r2 = 0.59 (P = 0.02) was achieved for the tumour depth measurement between AI and histological measured depths. Thus, addition of AI to the RCM-OCT device may expand its utility widely.
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Registered trials
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