ReviewJournal of clinical medicine2022
Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.Cancers · 2025Review
- 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
- Non-Invasive Imaging in Post-Radiotherapy Monitoring of Non-Melanoma Skin Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Article
- Transdermal Drug Delivery Systems: Different Generations and Dermatokinetic Assessment of Drug Concentration in Skin.Pharmaceutical medicine · 2024Review
- The Importance of In Vivo Reflectance Confocal Microscopy in a Case of Desmoplastic Melanoma.Life (Basel, Switzerland) · 2024Article
- Review
- Review
- Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms to identify quantitative biomarkers of facial skin ageing.Scientific reports · 2023Article
- Real-world use of a deep convolutional neural network to assist in the diagnosis of pyoderma gangrenosum.JAAD case reports · 2023Article
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Challenging Patterns of Atypical Dermatofibromas and Promising Diagnostic Tools for Differential Diagnosis of Malignant Lesions.Diagnostics (Basel, Switzerland) · 2023Review
- Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts.International journal of molecular sciences · 2023Review
- Paired-agent imaging as a rapidFrontiers in oncology · 2023Article
- Utilizing deep learning for dermal matrix quality assessment on in vivo line-field confocal optical coherence tomography images.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
- Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques.Computational intelligence and neuroscience · 2022Review
- Skin Diseases Classification Using Hybrid AI Based Localization Approach.Computational intelligence and neuroscience · 2022Article
- Molecular Imaging in Early Skin Cancer Detection: Advances, Limitations, and Future Directions.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence's impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.
Indexed as
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What OpenQuestion holds
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.