Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.
Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.
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DOI：
10.1002/uog.22122
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Papageorghiou AT
中科院分区：
文献类型：
--
作者：
Drukker L;Noble JA;Papageorghiou AT
人工智能（AI）使用数据和算法来得出与人类一样好甚至更好的结论。人工智能已经是我们日常生活的一部分;它是人脸识别技术、虚拟助手（如亚马逊Alexa、苹果Siri、谷歌助手和微软Cortana）中的语音识别和自动驾驶汽车的背后。人工智能软件已经能够在国际象棋，围棋和最近甚至扑克中击败世界冠军。与我们的社区相关，它是医疗保健创新的重要来源，已经帮助开发新药，支持临床决策并提供放射学质量保证。获得美国食品药品监督管理局或欧盟（即将纳入欧盟医疗器械法规）批准的医学图像分析AI应用程序列表正在迅速增长，并涵盖各种临床需求，例如使用智能手表检测心律失常或将关键成像研究的自动分类放在放射科医生工作列表的首位。深度学习是人工智能的主要工具，在图像模式识别方面表现得特别好，因此，对于严重依赖图像的医生（如超声科医生、放射技师和病理学家）来说，这是非常有益的。尽管产科和妇科超声是最常进行的两项成像研究，但到目前为止，人工智能对这一领域的影响甚微。尽管如此，人工智能在协助重复性超声任务方面仍有巨大的潜力，例如自动识别高质量的采集并提供即时质量保证。为了使这种潜力蓬勃发展，人工智能开发人员和超声专业人员之间的跨学科交流是必要的。在这篇文章中，我们探讨了医学成像AI的基本原理，从理论到应用，并向超声领域的医学专业人员介绍了一些关键术语。我们相信，更广泛的人工智能知识将有助于加速其融入医疗保健。版权所有2020作者。由John Wiley & Sons Ltd代表国际妇产科超声学会出版的《妇产科超声》。
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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影响因子：
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