The incidence of skin cancer has been gradually increasing worldwide since the 1960s. It is currently a health and economic problem for the different health systems. Dermoscopy is a non-invasive in vivo diagnostic technique, developed to study skin lesions. It improves the diagnostic accuracy of hyperpigmented lesions, as well as an early diagnosis of potentially malignant lesions, especially melanoma. The time spent on physical examination is not significantly increased. New applications have currently been discovered for this technique. Dermoscopy requires a learning process. Due to the complexity of the topic, the text has been divided into 2 parts to try to simplify its presentation. This first part will focus on the more technical aspects and the characteristics of the device called dermoscope. In the second part, 2 diagnostical methods will be presented along with their easy interpretation and usefulness in Primary Care.
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An intuitive explanation of dermoscopic structures by digitally reconstructed pathological horizontal top-down view images.
Sci Rep
December 2019
Department of Dermatology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-Ku, Hamamatsu, 431-3192, Japan.
Article Synopsis
- Dermoscopy is an effective method for diagnosing melanocytic lesions like nevi and melanoma, identifying features such as pigment networks and dots.
- Traditional 2D vertical images make it challenging to correlate horizontal dermoscopic views with vertical histopathological views.
- The study introduced a new digital reconstruction technique that converts multiple 2D sections into 3D images, aiding in the understanding of dermoscopic structures and serving as a valuable educational resource for beginners.
Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
J Eur Acad Dermatol Venereol
June 2020
Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
Background: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated.
Objective: To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists.
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
JAMA Dermatol
January 2019
Vienna Dermatologic Imaging Research Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria.
Importance: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.
Objective: To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.
Dermatoscopy of Neoplastic Skin Lesions: Recent Advances, Updates, and Revisions.
Curr Treat Options Oncol
September 2018
Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Dermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended.
View Article and Find Full Text PDFTriage amalgamated dermoscopic algorithm (TADA) for skin cancer screening.
Dermatol Pract Concept
April 2017
Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
Importance: Dermoscopic triage algorithms have been shown to improve beginners' abilities for identifying pigmented skin lesions requiring biopsy.
Objective: To estimate the diagnostic accuracy of the Triage Amalgamated Dermoscopic Algorithm (TADA) for pigmented and nonpigmented skin cancers. Secondarily, to compare TADAs performance to those of existing triage algorithms for the identification of pigmented skin cancers.
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