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West Ashley Library
9 a.m. - 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
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Phone: (843) 572-4094
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McClellanville Library
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Hurd/St. Andrews Library
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Phone: (843) 869-2355
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9 a.m. - 8 p.m.
Phone: (843) 552-6466
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Phone: (843) 722-7550
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Phone: (843) 805-6930
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Systematic review of machine learning for diagnosis and prognosis in dermatology.
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- Author(s): Thomsen, Kenneth; Iversen, Lars; Titlestad, Therese Louise; Winther, Ole
- Source:
Journal of Dermatological Treatment; Aug2020, Vol. 31 Issue 5, p496-510, 15p- Subject Terms:
- Source:
- Additional Information
- Abstract: Background: Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential for using such systems in dermatology. Objective: To evaluate the ways in which machine learning has been utilized in dermatology to date and provide an overview of the findings in current literature on the subject. Methods: We conducted a systematic review of existing literature, identifying the literature through a systematic search of the PubMed database. Two doctors assessed screening and eligibility with respect to pre-determined inclusion and exclusion criteria. Results: A total of 2175 publications were identified, and 64 publications were included. We identified eight major categories where machine learning tools were tested in dermatology. Most systems involved image recognition tools that were primarily aimed at binary classification of malignant melanoma (MM). Short system descriptions and results of all included systems are presented in tables. Conclusions: We present a complete overview of artificial intelligence implemented in dermatology. Impressive outcomes were reported in all of the identified eight categories, but head-to-head comparison proved difficult. The many areas of dermatology where we identified machine learning tools indicate the diversity of machine learning. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of Dermatological Treatment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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