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.
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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 and Allied Health Education (IMBE) Academic Structure > Institute of Medical, Biomedical and Allied Health Education (IMBE) > Centre for Clinical Education (INMECE ) |
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Journal or Publication Title: | Ultrasound Obstet Gynecol | ||||||||||||||||||||||||
ISSN: | 1469-0705 | ||||||||||||||||||||||||
Language: | eng | ||||||||||||||||||||||||
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Publisher License: | Creative Commons: Attribution 4.0 | ||||||||||||||||||||||||
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PubMed ID: | 32530098 | ||||||||||||||||||||||||
Web of Science ID: | WOS:000573931000003 | ||||||||||||||||||||||||
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URI: | https://openaccess.sgul.ac.uk/id/eprint/114781 | ||||||||||||||||||||||||
Publisher's version: | https://doi.org/10.1002/uog.22122 |
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