ArticleThe Journal of investigative dermatology2022
Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm.
Article in The Journal of investigative dermatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- The Diagnostic Accuracy of Ex Vivo Confocal Laser Scanning Microscopy for Basal Cell Carcinoma: A Systematic Review and Meta-analysis.Acta dermato-venereologica · 2026Pooled it
- A Prospective, Observational Study Evaluating the Diagnostic Performance of a Newly FDA-Approved Line-Field Optical Coherence Tomography (LC-OCT) in the Detection of Basal Cell Carcinoma.Lasers in surgery and medicine · 2026Observational
- The Diagnostic Accuracy of Ex Vivo Confocal Laser Scanning Microscopy for Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
- [Ex vivo confocal laser scanning microscopy and artificial intelligence for skin tumors].Dermatologie (Heidelberg, Germany) · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- One-Stop Shop: Diagnosis and Treatment of Basal Cell Carcinoma in One Step.Journal of clinical medicine · 2024Review
- High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.Journal of biophotonics · 2024Article
- Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy.Experimental dermatology · 2023Article
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- A Feasibility Study for Immediate Histological Assessment of Various Skin Biopsies Using Ex Vivo Confocal Laser Scanning Microscopy.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
Ex vivo confocal microscopy (EVCM) generates digitally colored purple-pink images similar to H&E without time-consuming tissue processing. It can be used during Mohs surgery for rapid detection of basal cell carcinoma (BCC); however, reading EVCM images requires specialized training. An automated approach using a deep learning algorithm for BCC detection in EVCM images can aid in diagnosis. A total of 40 BCCs and 28 negative (not-BCC) samples were collected at Memorial Sloan Kettering Cancer Center to create three training datasets: (i) EVCM image dataset (663 images), (ii) H&E image dataset (516 images), and (iii) a combination of the two datasets. A total of seven BCCs and four negative samples were collected to create an EVCM test dataset (107 images). The model trained with the EVCM dataset achieved 92% diagnostic accuracy, similar to the H&E model (93%). The area under the receiver operator characteristic curve was 0.94, 0.95, and 0.94 for EVCM-, H&E-, and combination-trained models, respectively. We developed an algorithm for automatic BCC detection in EVCM images (comparable accuracy to dermatologists). This approach could be used to assist with BCC detection during Mohs surgery. Furthermore, we found that a model trained with only H&E images (which are more available than EVCM images) can accurately detect BCC in EVCM images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.