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

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.	中國抗癌協會, 國家腫瘤臨床醫學研究中心(天津醫科大學腫瘤醫院). 中國女性乳腺癌篩查指南. 中國腫瘤臨床, 2019, 46(9): 430-432.
4.	李江, 唐威, 李霓, 等. 乳腺癌篩查指南方法學質量和報告質量的系統評價. 中國循證醫學雜志, 2018, 18(6): 629-636.
5.	高鷹. 中國女性乳腺癌篩查策略優化研究及衛生經濟學評價. 天津: 天津醫科大學, 2016.
6.	韓優莉, 王曉燕. 超聲檢查用于亞洲女性乳腺癌篩查衛生經濟學研究的系統評價. 中國腫瘤, 2018, 27(7): 553-560.
7.	劉雁霞, 陳曉嬌. 31 057例適齡婦女兩癌篩查數據分析. 中外女性健康研究, 2019, (14): 197-198.
8.	張群華. 擁抱互聯網醫療和人工智能融合的高光時刻. 中國普外基礎與臨床雜志, 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. 人工智能時代超聲醫學新發展. 第二軍醫大學學報, 2019, 40(5): 478-482.
16.	劉伶俐, 王端, 王力鋼. 醫療人工智能應用中的倫理問題及應對. 醫學與哲學, 2020, 41(14): 28-32.
17.	余紹德. 卷積神經網絡和遷移學習在癌癥影像分析中的研究. 深圳: 中國科學院大學(中國科學院深圳先進技術研究院), 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. 中國抗癌協會, 國家腫瘤臨床醫學研究中心(天津醫科大學腫瘤醫院). 中國女性乳腺癌篩查指南. 中國腫瘤臨床, 2019, 46(9): 430-432.
4. 李江, 唐威, 李霓, 等. 乳腺癌篩查指南方法學質量和報告質量的系統評價. 中國循證醫學雜志, 2018, 18(6): 629-636.
5. 高鷹. 中國女性乳腺癌篩查策略優化研究及衛生經濟學評價. 天津: 天津醫科大學, 2016.
6. 韓優莉, 王曉燕. 超聲檢查用于亞洲女性乳腺癌篩查衛生經濟學研究的系統評價. 中國腫瘤, 2018, 27(7): 553-560.
7. 劉雁霞, 陳曉嬌. 31 057例適齡婦女兩癌篩查數據分析. 中外女性健康研究, 2019, (14): 197-198.
8. 張群華. 擁抱互聯網醫療和人工智能融合的高光時刻. 中國普外基礎與臨床雜志, 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. 人工智能時代超聲醫學新發展. 第二軍醫大學學報, 2019, 40(5): 478-482.
16. 劉伶俐, 王端, 王力鋼. 醫療人工智能應用中的倫理問題及應對. 醫學與哲學, 2020, 41(14): 28-32.
17. 余紹德. 卷積神經網絡和遷移學習在癌癥影像分析中的研究. 深圳: 中國科學院大學(中國科學院深圳先進技術研究院), 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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