Artificial Intelligence in Breast US Diagnosis and Report Generation.

Journal: Radiology. Artificial intelligence
Published Date: Jun 18, 2025

Abstract

Purpose To develop and evaluate an artificial intelligence (AI) system for generating breast ultrasound (BUS) reports. Materials and Methods This retrospective study included 104,364 cases from three hospitals (January 2020-December 2022). The AI system was trained on 82,896 cases, validated on 10,385 cases, and tested on an internal set (10,383 cases) and two external sets (300 and 400 cases). Under blind review, three senior radiologists (> 10 years of experience) evaluated AI-generated reports and those written by one midlevel radiologist (7 years of experience), as well as reports from three junior radiologists (2-3 years of experience) with and without AI assistance. The primary outcomes included the acceptance rates of Breast Imaging Reporting and Data System (BI-RADS) categories and lesion characteristics. Statistical analysis included one-sided and two-sided McNemar tests for non-inferiority and significance testing. Results In external test set 1 (300 cases), the midlevel radiologist and AI system achieved BI-RADS acceptance rates of 95.00% [285/300] versus 92.33% [277/300] ( < .001; non-inferiority test with a prespecified margin of 10%). In external test set 2 (400 cases), three junior radiologists had BI-RADS acceptance rates of 87.00% [348/400] versus 90.75% [363/400] ( = .06), 86.50% [346/400] versus 92.00% [368/400] ( = .007), and 84.75% [339/400] versus 90.25% [361/400] ( = .02) with and without AI assistance, respectively. Conclusion The AI system performed comparably to a midlevel radiologist and aided junior radiologists in BI-RADS classification. ©RSNA, 2025.

Authors

  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Hongtian Tian
    Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Huaiyu Wu
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Xiliang Zhu
    Department of Cardiovascular Surgery, Henan Province People's Hospital, Fuwai Central China Cardiovascular Hospital, Henan Cardiovascular Hospital and Zhengzhou University, Zhengzhou, China.
  • Rusi Chen
    From the Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, Italy (F.B., J.B., R.C., V.F., E.S., M.T., F.V., T.A.W., P.M., S.M.).
  • Ao Chang
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
  • Yanlin Chen
  • Haoran Dou
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
  • Ruobing Huang
    Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. Electronic address: ruobing.huang@eng.ox.ac.uk.
  • Jun Cheng
    School of Electrical and Information Technology, Yunnan Minzu University, Kunming, Yunnan 650500, PR China. Electronic address: jcheng6819@126.com.
  • Yongsong Zhou
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518073, China.
  • Rui Gao
    School of Control Science and Engineering, Shandong University, Jinan, China.
  • Keen Yang
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Guoqiu Li
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Dong Ni
  • Fajin Dong
    Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China.
  • Jinfeng Xu
    Department of Ultrasound, The Second Clinical Medical College，Jinan University, Guangdong, China.
  • Ning Gu
    Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals