A non-invasive end-to-end intelligent assistance system for breast ultrasound.
Authors
Affiliations (30)
Affiliations (30)
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
- Department of Breast Surgery, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Department of Ultrasound Medicine, Dazhou Central Hospital, Dazhou, Sichuan, China.
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
- Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
- Department of Surgery, Shantou University Medical College, Shantou, Guangdong, China.
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China.
- Information Department, Dazhou Central Hospital, Dazhou, Sichuan, China.
- Department of Oncology, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
- Department of Ultrasound Medicine, Dachuan District People's Hospital, Dazhou, Sichuan, China.
- Department of Ultrasound Medicine, Kaijiang County People's Hospital, Dazhou, Sichuan, China.
- Department of Ultrasound Medicine, Tongchuan District People's Hospital, Dazhou, Sichuan, China.
- Functional Department, Wanyuan Central Hospital, Dazhou, Sichuan, China.
- Department of Ultrasound Imaging, Dazhu County People's Hospital, Dazhou, Sichuan, China.
- Functional Department, Xuanhan County Third People's Hospital, Dazhou, Sichuan, China.
- Functional Department, Quxian County People's Hospital, Dazhou, Sichuan, China.
- Department of Ultrasound Medicine, Nanchong Central Hospital, Dazhou, Sichuan, China.
- Functional Department, XinBei Community Healthcare Center of Chengdu High-Tech Zone, Dazhou, Sichuan, China.
- Department of Clinical Laboratory, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
- Henan International Joint Laboratory of Non-coding RNA and Tumor Metabolism, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
- Department of Clinical Medicine, Mudanjiang Medical University, Mudanjiang, Heilongjiang, China.
- Department of Integrated Traditional Chinese and Western Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Intelligent Medical Engineering, North Sichuan Medical College, Nanchong, Sichuan, China.
- Department of Hepatobiliary Surgery, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. [email protected].
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. [email protected].
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China. [email protected].
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China. [email protected].
- Intelligent Medical Engineering, North Sichuan Medical College, Nanchong, Sichuan, China. [email protected].
Abstract
Although artificial intelligence enhances medical image classification to effectively improve lesion diagnosis accuracy and efficiency, it still faces generalization challenges in breast ultrasound and its clinical potential remains underutilized in complex real-world scenarios. Here, we develop and test an end-to-end breast intelligent recognition device (BIRD) for women across diverse institution/population datasets. The accuracy of BIRD in the internal test set is 0.837 (95% confidence interval: 0.827-0.846), which significantly improves radiologists' accuracy in 2 reader studies (P < 0.05). BIRD is applied in breast cancer screening for 6,817 individuals and shows high consistency (Cohen's kappa: 0.702 (95% confidence interval: 0.628-0.777)) with clinical assessments in real-world application across 107 hospitals. Pathological and molecular subtype models developed using data from five hospitals also exhibit satisfactory classification performance. These findings confirm BIRD's ability to improve diagnostic accuracy, assist screening, and characterize breast lesions, facilitating clinical adoption in breast health practice. The Chinese Clinical Trial Register: ChiCTR2300073777.