Application value of artificial intelligence-assisted diagnosis model in ultrasound diagnosis of breast nodules
10.3760/cma.j.issn.1006-9801.2019.10.001
- VernacularTitle: 人工智能辅助诊断模型在乳腺结节超声诊断中的应用价值
- Author:
Hongtao JI
1
;
Qiang ZHU
1
;
Conggui GAN
2
;
Shuai ZHOU
1
;
Yun CHENG
1
;
Mingchang ZHAO
2
Author Information
1. Department of Ultrasound, Beijing Tongren Hospital of Capital Medical University, Beijing 100730, China
2. Chison Medical Technologies Co. LTD, Wuxi 214142, China
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Convolutional neural network;
Artificial intelligence;
Auxiliary diagnosis
- From:
Cancer Research and Clinic
2019;31(10):649-652
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of the convolutional neural network (CNN)-based artificial intelligence-assisted diagnosis model in the ultrasound differentiation diagnosis of benign and malignant breast nodules.
Methods:A total of 7 334 ultrasound images from 1 351 patients with breast nodules including 807 benign cases and 544 malignant cases were retrieved by using the CNN-based artificial intelligence-assisted diagnosis model from Beijing Tongren Hospital of Capital Medical University ultrasound images database between December 2006 and July 2017. The study included training subset (6 162 images), verification subset (555 images), and test subset (617 images), which were performed in the artificial intelligence-assisted diagnosis model. The outcome results of test subset in diagnosis model were compared with the pathological results. The sensitivity, specificity and accuracy of the artificial intelligence-assisted diagnosis model were calculated.
Results:After the test of 617 images, the model diagnostic results could be automatically output with a rectangular frame indicating the nodule position, benign and malignant diagnosis, benign and malignant probability values. The diagnosis time was approximately 4 seconds for each nodule. The sensitivity, specificity and accuracy of the diagnostic model in differentiating benign and malignant breast nodules were 84.1%, 95.0% and 91.2% , respectively.
Conclusion:The CNN-based artificial intelligence-assisted diagnosis model has satisfactory results in the differentiation diagnosis of the benign breast nodules and the malignant ones, which indicating the promising application prospect.
