期刊
JOURNAL OF CLINICAL MEDICINE
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/jcm11020429
关键词
dermatology; dermoscopy; in vivo; confocal microscopy; deep learning; artificial intelligence; skin cancer; artifact
Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed for identifying various skin diseases, but the diagnosis process may be subjective. Deep learning technologies and machine learning algorithms developed in recent years provide a more objective approach to RCM image analysis, reducing artifacts and evaluation times.
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.
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