• Zeynettin Akkus, Jason Cai, Arunnit Boonrod, Atefeh Zeinoddini, Alexander D Weston, Kenneth A Philbrick, and Bradley J Erickson.
• Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, Minnesota. Electronic address: akkus.zeynettin@mayo.edu.
• J Am Coll Radiol. 2019 Sep 1; 16 (9 Pt B): 1318-1328.

AbstractUltrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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