Develop mobile phone terminal application software DeepBC based on breast ultrasound artificial intelligence_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

CHEN Yao ¹ ,  LÜ Qing ¹ , QI Xiaofeng ²

1. Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. School of Computing, Sichuan University, Chengdu 610065, P. R. China;

Corresponding author：

LÜ Qing, Email: lqlq1963@163.com

Keywords：

breast cancer; artificial intelligence; ultrasound; mobile phone terminal

DOI：

10.7507/1007-9424.202104100

Video：

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Objective To develop mobile phone terminal application software using artificial intelligence (AI) model of breast ultrasound so as to provide an opportunity for early diagnosis of patients with breast cancer irrespective of time and space. Methods The ultrasonic electronic images of patients underwent operation in the Department of Breast Surgery of West China Hospital of Sichuan University from January 2018 to April 2019 were collected. The neural network deep learning algorithm was used to train and test the breast ultrasonic electronic images at a ratio of 4∶1 to establish DeepBC model, and a mobile phone terminal application software was developed according to the trained DeepBC model, which included image reconstruction module, image classification module, and missed diagnosis module to identify and diagnose the uploaded ultrasonic electronic images. Results A total of 4 128 ultrasonic electronic images were collected in this study, including 3 302 in the training set and 826 in the test set. The accuracy, sensitivity, specificity, false positive rate, and false negative rate of the DeepBC model for the identification of malignant and non-malignant lesions in the breast ultrasound images were 93.70%, 93.10%, 94.08%, 5.92%, and 6.90%, respectively. The optimal cut-off value was 92.31% by receiver operating characteristic curve of DeepBC model and the area of receiver operating characteristic curve was 0.987. The DeepBC mobile phone terminal application software was developed according to the DeepBC model, and the web page was released in the mobile wechat. So far, more than 10 000 people had uploaded ultrasonic electronic images on the wechat web page, and the diagnosis had exceeded 30 000 times. Conclusions In this study, an AI DeepBC model is established successfully based on ultrasonic electronic images, each module of mobile phone terminal application software runs well and independently. And web page is simple and contents are easy to be comprehended.

Citation： CHEN Yao, LÜ Qing, QI Xiaofeng. Develop mobile phone terminal application software DeepBC based on breast ultrasound artificial intelligence. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2022, 29(1): 46-50. doi: 10.7507/1007-9424.202104100 Copy

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2.	McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
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6.	韩优莉, 王晓燕. 超声检查用于亚洲女性乳腺癌筛查卫生经济学研究的系统评价. 中国肿瘤, 2018, 27(7): 553-560.
7.	刘雁霞, 陈晓娇. 31 057例适龄妇女两癌筛查数据分析. 中外女性健康研究, 2019, (14): 197-198.
8.	张群华. 拥抱互联网医疗和人工智能融合的高光时刻. 中国普外基础与临床杂志, 2020, 27(9): 1049-1051.
9.	Lotter W, Sorensen G, Cox D. A multi-scale CNN and curriculum learning strategy for mammogram classification. Springer: International Publishing, 2017.
10.	Niklason LT, Christian BT, Niklason LE, et al. Digital tomosynthesis in breast imaging. Radiology, 1997, 205(2): 399-406.
11.	Suckling J, Parker J, Dance DR. The mammographic image analysis society digital mammogram database. http://peipa.essex.ac.uk/info/mias.html.
12.	Han S, Kang HK, Jeong JY, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol, 2017, 62(19): 7714-7728.
13.	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. IEEE Computer Society, 2014.
14.	Cho E, Kim EK, Song MK, et al. Application of computer-aided diagnosis on breast ultrasonography: evaluation of diagnostic performances and agreement of radiologists according to different levels of experience. J Ultrasound Med, 2018, 37(1): 209-216.
15.	徐琪. 人工智能时代超声医学新发展. 第二军医大学学报, 2019, 40(5): 478-482.
16.	刘伶俐, 王端, 王力钢. 医疗人工智能应用中的伦理问题及应对. 医学与哲学, 2020, 41(14): 28-32.
17.	余绍德. 卷积神经网络和迁移学习在癌症影像分析中的研究. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2018.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
3. 中国抗癌协会, 国家肿瘤临床医学研究中心(天津医科大学肿瘤医院). 中国女性乳腺癌筛查指南. 中国肿瘤临床, 2019, 46(9): 430-432.
4. 李江, 唐威, 李霓, 等. 乳腺癌筛查指南方法学质量和报告质量的系统评价. 中国循证医学杂志, 2018, 18(6): 629-636.
5. 高鹰. 中国女性乳腺癌筛查策略优化研究及卫生经济学评价. 天津: 天津医科大学, 2016.
6. 韩优莉, 王晓燕. 超声检查用于亚洲女性乳腺癌筛查卫生经济学研究的系统评价. 中国肿瘤, 2018, 27(7): 553-560.
7. 刘雁霞, 陈晓娇. 31 057例适龄妇女两癌筛查数据分析. 中外女性健康研究, 2019, (14): 197-198.
8. 张群华. 拥抱互联网医疗和人工智能融合的高光时刻. 中国普外基础与临床杂志, 2020, 27(9): 1049-1051.
9. Lotter W, Sorensen G, Cox D. A multi-scale CNN and curriculum learning strategy for mammogram classification. Springer: International Publishing, 2017.
10. Niklason LT, Christian BT, Niklason LE, et al. Digital tomosynthesis in breast imaging. Radiology, 1997, 205(2): 399-406.
11. Suckling J, Parker J, Dance DR. The mammographic image analysis society digital mammogram database. http://peipa.essex.ac.uk/info/mias.html.
12. Han S, Kang HK, Jeong JY, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol, 2017, 62(19): 7714-7728.
13. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. IEEE Computer Society, 2014.
14. Cho E, Kim EK, Song MK, et al. Application of computer-aided diagnosis on breast ultrasonography: evaluation of diagnostic performances and agreement of radiologists according to different levels of experience. J Ultrasound Med, 2018, 37(1): 209-216.
15. 徐琪. 人工智能时代超声医学新发展. 第二军医大学学报, 2019, 40(5): 478-482.
16. 刘伶俐, 王端, 王力钢. 医疗人工智能应用中的伦理问题及应对. 医学与哲学, 2020, 41(14): 28-32.
17. 余绍德. 卷积神经网络和迁移学习在癌症影像分析中的研究. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2018.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Develop mobile phone terminal application software DeepBC based on breast ultrasound artificial intelligence

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