Minireviews
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Feb 28, 2019; 11(2): 19-26
Published online Feb 28, 2019. doi: 10.4329/wjr.v11.i2.19
Artificial intelligence in breast ultrasound
Ge-Ge Wu, Li-Qiang Zhou, Jian-Wei Xu, Jia-Yu Wang, Qi Wei, You-Bin Deng, Xin-Wu Cui, Christoph F Dietrich
Ge-Ge Wu, Li-Qiang Zhou, Jia-Yu Wang, Qi Wei, You-Bin Deng, Xin-Wu Cui, Christoph F Dietrich, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Jian-Wei Xu, Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
Christoph F Dietrich, Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Würzburg, Würzburg 97980, Germany
Author contributions: Cui XW established the design and conception of the paper; Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, and Dietrich CF explored the literature data; Wu GG provided the first draft of the manuscript, which was discussed and revised critically for intellectual content by Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, and Dietrich CF; all authors discussed the statement and conclusions and approved the final version to be published.
Conflict-of-interest statement: We declare that we do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Xin-Wu Cui, MD, PhD, Professor, Deputy Director, Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan 430030, Hubei Province, China. cuixinwu@live.cn
Telephone: +86-27-83663754 Fax: +86-27-83663754
Received: November 29, 2018
Peer-review started: November 30, 2018
First decision: January 4, 2019
Revised: January 14, 2019
Accepted: January 26, 2019
Article in press: January 27, 2019
Published online: February 28, 2019
Processing time: 91 Days and 16.6 Hours
Abstract

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

Keywords: Breast; Ultrasound; Artificial intelligence; Machine learning; Deep learning

Core tip: Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. In this review, we summarize the current knowledge of AI in breast ultrasound, including the technical aspects, and its applications in the differentiation between benign and malignant breast masses. In the meanwhile, we also discuss the future perspectives, such as combining with elastography and contrast-enhanced ultrasound, to improve the performance of AI in breast ultrasound.