ArticleThe Journal of investigative dermatology2022
Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.
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 25 papers.
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Who cites it
25 citing papers in PubMed, 60 citations in OpenAlex.
- Deep learning analysis of reflectance confocal microscopy for basal cell carcinoma diagnosis.JAAD international · 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
- Laser Therapy in Basal Cell Carcinoma: Current Evidence, Literature Gaps and Future Perspectives.Bioengineering (Basel, Switzerland) · 2026Review
- Global trends and academic landscapes of AI applications in basal cell carcinoma research: a bibliometric analysis.Frontiers in oncology · 2026Review
- Automated Detection of Benign and Malignant Skin Lesions from Reflectance Confocal Microscopy Images Using Deep Learning.JID innovations : skin science from molecules to population health · 2025Article
- [In vivo reflectance confocal microscopy and keratinocyte skin cancer : Reflectance confocal microscopy and artificial intelligence].Dermatologie (Heidelberg, Germany) · 2025Review
- Emerging Technologies for Timely Point-of-Care Diagnostics of Skin Cancer.Global challenges (Hoboken, NJ) · 2025Review
- Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- 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.Clinical and experimental dermatology · 2024Article
- Article
- Review
- Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.Journal of Cancer · 2024Article
- Optically Guided High-Frequency Ultrasound to Differentiate High-Risk Basal Cell Carcinoma Subtypes: A Single-Centre Prospective Study.Journal of clinical medicine · 2023Article
- Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy.Experimental dermatology · 2023Article
- Article
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- Non-invasive imaging techniques in presurgical margin assessment of basal cell carcinoma: Current evidence.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.Journal of personalized medicine · 2022Article
- Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.Journal of biomedical optics · 2022Article
- An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 7 countries.
Funding
Abstract
Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7% (stack level) and 88.3% (lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, the model achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer.
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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.