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Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.

Drukker, L; Noble, JA; Papageorghiou, AT (2020) Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol, 56 (4). pp. 498-505. ISSN 1469-0705 https://doi.org/10.1002/uog.22122
SGUL Authors: Papageorghiou, Aris

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Abstract

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Item Type: Article
Additional Information: © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Artificial Intelligence, Female, Gynecology, Humans, Obstetrics, Pregnancy, Ultrasonography, Humans, Ultrasonography, Gynecology, Obstetrics, Pregnancy, Artificial Intelligence, Female, 1114 Paediatrics and Reproductive Medicine, Obstetrics & Reproductive Medicine
SGUL Research Institute / Research Centre: Academic Structure > Institute of Medical & Biomedical Education (IMBE)
Academic Structure > Institute of Medical & Biomedical Education (IMBE) > Centre for Clinical Education (INMECE )
Journal or Publication Title: Ultrasound Obstet Gynecol
ISSN: 1469-0705
Language: eng
Dates:
DateEvent
1 October 2020Published
12 June 2020Published Online
1 June 2020Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
49038Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
EP/M013774/1Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/R013853/1Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
ERC-ADG-2015 694581European Research Councilhttp://dx.doi.org/10.13039/501100000781
UNSPECIFIEDNational Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)UNSPECIFIED
MR/P027938/1Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
OPP1197123Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
PubMed ID: 32530098
Web of Science ID: WOS:000573931000003
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/114781
Publisher's version: https://doi.org/10.1002/uog.22122

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