Artificial Intelligence in Head and Neck Imaging: A Glimpse into the Future.


Journal

Neuroimaging clinics of North America
ISSN: 1557-9867
Titre abrégé: Neuroimaging Clin N Am
Pays: United States
ID NLM: 9211377

Informations de publication

Date de publication:
Aug 2020
Historique:
entrez: 1 7 2020
pubmed: 1 7 2020
medline: 27 4 2021
Statut: ppublish

Résumé

Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.

Identifiants

pubmed: 32600636
pii: S1052-5149(20)30027-7
doi: 10.1016/j.nic.2020.04.004
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

359-368

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure The authors have nothing to disclose.

Auteurs

Kyle Werth (K)

Department of Radiology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Mailstop 4032, Kansas City, KS 66160, USA.

Luke Ledbetter (L)

Department of Radiology, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621D, Los Angeles, CA 90095, USA. Electronic address: lledbetter@mednet.ucla.edu.

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Classifications MeSH