Literature DB >> 32428608

Deep Learning for Dermatologists: Part II. Current Applications.

Pranav Puri1, Nneka Comfere2, Lisa A Drage3, Huma Shamim3, Spencer A Bezalel3, Mark R Pittelkow4, Mark D P Davis3, Michael Wang5, Aaron R Mangold4, Megha M Tollefson3, Julia S Lehman6, Alexander Meves3, James A Yiannias7, Clark C Otley3, Rickey E Carter8, Olayemi Sokumbi9, Matthew R Hall10, Alina G Bridges6, Dennis H Murphree11.   

Abstract

Due to a convergence of the availability of large datasets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence (AI) has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning (DL) has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties including radiology, cardiology and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning in order to effectively use new applications as well as to better gauge their utility and limitations. In this second article of a two part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.
Copyright © 2020. Published by Elsevier Inc.

Keywords:  artificial intelligence; deep learning; dermatology; machine learning

Year:  2020        PMID: 32428608     DOI: 10.1016/j.jaad.2020.05.053

Source DB:  PubMed          Journal:  J Am Acad Dermatol        ISSN: 0190-9622            Impact factor:   11.527


  4 in total

1.  Identification of metastatic primary cutaneous squamous cell carcinoma utilizing artificial intelligence analysis of whole slide images.

Authors:  Jaakko S Knuutila; Pilvi Riihilä; Antti Karlsson; Mikko Tukiainen; Lauri Talve; Liisa Nissinen; Veli-Matti Kähäri
Journal:  Sci Rep       Date:  2022-06-14       Impact factor: 4.996

2.  β3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma.

Authors:  Enrica Quattrocchi; Sindhuja Sominidi-Damodaran; Dennis H Murphree; Alexander Meves
Journal:  Int J Dermatol       Date:  2020-08-09       Impact factor: 2.736

3.  COVID-19: An opportunity to build dermatology's digital future.

Authors:  Pranav Puri; Nneka Comfere; Mark R Pittelkow; Spencer A Bezalel; Dennis H Murphree
Journal:  Dermatol Ther       Date:  2020-09-04       Impact factor: 3.858

Review 4.  Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology.

Authors:  Ana Maria Malciu; Mihai Lupu; Vlad Mihai Voiculescu
Journal:  J Clin Med       Date:  2022-01-14       Impact factor: 4.241

  4 in total

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