Application and Prospect of AI and ABVS-based in Breast Ultrasound Diagnosis

Rui Chen, MM; Fangqi Guo, MM; Jia Guo, MD; Jiaqi Zhao, MD

doi:10.37015/AUDT.2023.230017

ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY >

2023 , Vol. 7 >Issue 2: 130 - 135

DOI: https://doi.org/10.37015/AUDT.2023.230017

Review Articles

Application and Prospect of AI and ABVS-based in Breast Ultrasound Diagnosis

Rui Chen, MM ,
Fangqi Guo, MM ,
Jia Guo, MD ,
Jiaqi Zhao, MD

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^a Department of Ultrasound, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
^b Department of Ultrasound, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China

Department of Ultrasound, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, 1279 Sanmen Road, Shanghai, China, e-mail: qiqiblue67@163.com

Received date: 2023-03-30

Revised date: 2023-04-20

Accepted date: 2023-04-22

Online published: 2023-04-27

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Abstract

Breast cancer is the most common malignancy and the leading cause of death for women. Ultrasound is the main tool for breast cancer screening, but it can be influenced by the subjective factors of sonographers. With the continuous development of medical technology and artificial intelligence (AI), the application of breast ultrasound imaging technology is becoming increasingly widespread. Among them, the application of AI and automated breast volume scanning (ABVS) brings new opportunities and challenges for ultrasound diagnosis of breast diseases, while making breast ultrasound diagnosis more accurate and efficient. This article explores the application and prospects of AI and ABVS in ultrasound diagnosis of breast diseases.

Key words： Artificial intelligence; Automated breast volume scanner; Breast cancer; Ultrasound diagnosis; Deep learning

Cite this article

Rui Chen, MM , Fangqi Guo, MM , Jia Guo, MD , Jiaqi Zhao, MD . Application and Prospect of AI and ABVS-based in Breast Ultrasound Diagnosis[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2023 , 7(2) : 130 -135 . DOI: 10.37015/AUDT.2023.230017

References

[1]	Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018; 68:7-30.
[2]	Zheng RT, Sun KX, Zhang SW, Zeng HM, Zou XN, Chen R, et al. Analysis of the prevalence of malignant tumors in China in 2015. Chinese Journal of Oncology 2019; 41:19-28. [In Chinese]
[3]	Li WQ, Li R, Liu PF, Huang YB. Discussion of breast cancer screening model in China. Chin J Epidemiol 2016; 37:1039-1043. [In Chinese]
[4]	Wu Z, Liu Y, Li X, Song B, Ni C, Lin F. Factors associated with breast cancer screening participation among women in mainland China: a systematic review. BMJ Open 2019; 9:e028705.
[5]	Breast Cancer Professional Committee of China Anti-Cancer Association. Guidelines for Breast Cancer Screening and Early Diagnosis in China. Chin Oncol 2022; 32:363-372. [In Chinese]
[6]	Fan MM. Application analysis of artificial intelligence in computer technology. The Frontiers of Society, Science and Technology 2020; 2:131-136.
[7]	Lawrence DR, Palacios-González C, Harris J. Artificial intelligence. Camb Q Healthc Ethics 2016; 25:250-261.
[8]	Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ. Artificial intelligence in breast imaging. Clin Radiol 2019; 74:357-366.
[9]	Gu P, Lee WM, Roubidoux MA, Yuan J, Wang X, Carson PL. Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 2016; 65:51-58.
[10]	Xu Y, Wang Y, Yuan J, Cheng Q, Wang X, Carson PL. Medical breast ultrasound image segmentation by machine learning. Ultrasonics 2019; 91:1-9.
[11]	Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kasahara M, et al. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol 2019; 37:466-472.
[12]	Park HJ, Kim SM, La Yun B, Jang M, Kim B, Jang JY, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist. Medicine (Baltimore) 2019; 98:e14146.
[13]	Qiao M, Guo Y, Zhou S, Chang C, Wang Y. Breast calcifications detection based on radiofrequency signals by quantitative ultrasound multi-parameter fusion. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2019:6196-6199.
[14]	Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, et al. Lymph node metastasis prediction from primary breast cancer us images using deep learning. Radiology 2020; 294:19-28.
[15]	Bove S, Comes MC, Lorusso V, Cristofaro C, Didonna V, Gatta G, et al. A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients. Sci Rep 2022; 12:7914.
[16]	Sannachi L, Gangeh M, Tadayyon H, Gandhi S, Wright FC, Slodkowska E, et al. Breast cancer treatment response monitoring using quantitative ultrasound and texture analysis: comparative analysis of analytical models. Transl Oncol 2019; 12:1271-1281.
[17]	Choi WJ, Cha JH, Kim HH, Shin HJ, Kim H, Chae EY, et al. Comparison of automated breast volume scanning and hand- held ultrasound in the detection of breast cancer: an analysis of 5,566 patient evaluations. Asian Pac J Cancer Prev 2014; 15:9101-9105.
[18]	Zheng FY, Yan LX, Huang BJ, Xia HS, Wang X, Lu Q, et al. Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner. Eur J Radiol 2015; 84:2123-2129.
[19]	Lagendijk M, Vos EL, Ramlakhan KP, Verhoef C, Koning AHJ, van Lankeren W, et al. Breast and tumour volume measurements in breast cancer patients using 3-D automated breast volume scanner images. World J Surg 2018; 42:2087-2093.
[20]	Huang J, Zhang Y, Fu R, Lu M. Accuracy and repeatability of automated breast volume scanner imaging in measuring the volume of irregular objects. Practical Journal of Clinical Medicine 2015; 12:93-95. [In Chinese]
[21]	Zhou N, Zeng HQ, Liu SH, Tang H, Jin XY, et al. The value of ABVS in evaluating the response of neoadjuvant chemotherapy for different molecular subtypes of breast cancer. Journal of Chinese Physician 2020;1342-1346. [In Chinese]
[22]	Van Zelst JCM, Tan T, Clauser P, Domingo A, Dorrius MD, Drieling D, et al. Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts. Eur Radiol 2018; 28:2996-3006.
[23]	Xu X, Bao L, Tan Y, Zhu L, Kong F, Wang W. 1000-case reader study of radiologists' performance in interpretation of automated breast volume scanner images with a computer-aided detection system. Ultrasound Med Biol 2018; 44:1694-1702.

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