JCNRJournal of Clinical and Nursing Research2208-36852208-3693Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcnr.v10i6.15570ArticleThe Application of Dynamic Artificial Intelligence-Assisted Ultrasound Examination in the Diagnosis of Breast Lesionshttps://artdesignp.com/journal/JCNR/10/6/10.26689/jcnr.v10i6.15570SunZhengying,SunMeng20261062026-06-30Early accurate diagnosis of breast lesions is the core link to reducing the harm of malignant breast lesions and improving patients’ prognosis. Relying on deep learning, real-time image recognition, and dynamic image analysis technologies, dynamic artificial intelligence-assisted ultrasound examination can intelligently analyze dynamic breast ultrasound images, automatically locate lesions, extract imaging features of lesions, and assist in differentiating benign and malignant lesions, making up for the inherent shortcomings of traditional ultrasound examination from a technical perspective. On this basis, this paper studies the application of dynamic artificial intelligence-assisted ultrasound examination in the diagnosis of breast lesions, analyzes the practical problems existing in traditional ultrasound examination for breast lesion diagnosis, explains the clinical application value of dynamic artificial intelligence-assisted ultrasound examination, and puts forward targeted application countermeasures for technical popularization, so as to promote the steady development of breast lesion diagnosis toward standardization, precision and high efficiency.

Zhao Y, 2025, Preliminary Application of Breast Ultrasound AI Recognition System in Real-Time Breast Ultrasonic Examination, thesis, Hebei Medical University.

Jia X, 2024, Research on Migration Decision-Making Method Based on Multi-Level Parameters, thesis, Hefei University of Technology.

Li L, 2023, Application Value of Artificial Intelligence Combined with Ultrasound Images in Classification of Breast Lesions, thesis, Kunming Medical University.

Zhou H, 2024, Research on Segmentation and Classification of MRI Breast Lesions Based on Deep Learning, thesis, Shandong Technology and Business University.

Liu Y, 2024, Study on Efficacy Prediction of Neoadjuvant Therapy for Breast Cancer Using Terahertz and Ultrasound Data, thesis, University of Electronic Science and Technology of China.

Li X, Zhao J, Ren J, 2024, Application of Dual-Mode Ultrasound Deep Learning Prediction Model in Breast Cancer Diagnosis. Chinese Journal of Clinical Research, 37(03): 365–369 + 374.

Li X, 2024, Research on Application of Dual-Mode Ultrasound Deep Learning Prediction Model in Breast Cancer Diagnosis, thesis, Jinzhou Medical University.

Li W, 2024, Diagnostic Value of Artificial Intelligence S-Detect Combined with Contrast-Enhanced Ultrasound in Benign and Malignant Differentiation of Breast Masses, thesis, Shihezi University.

Shen J, Liu Y, Mo M, et al., 2023, Effectiveness of Artificial Intelligence-Assisted Ultrasound for Breast Lesion Detection in Chinese Women. China Oncology, 33(11): 1002–1008.

Jin Q, Lü J, Xu L, 2023, Influences of Different Ultrasonic Imaging Parameters on S-Detect Technology for Benign and Malignant Identification of Breast Nodules. Journal of Practical Medicine, 39(22): 2891–2897.

Wang H, Yang X, Chen F, 2023, Research Progress of Artificial Intelligence in Auxiliary Diagnosis of Breast Imaging. Chinese Journal of Medical Imaging, 31(10): 1113–1116.

Liao W, Zhu Y, Xu J, 2023, Patent Analysis of Breast Cancer Analysis and Prediction Based on Artificial Intelligence. China Science and Technology Information, 2023(18): 56–58.

Bian J, Jiang J, He X, 2023, Application Value of Yizhun Intelligent Software in Benign and Malignant Differentiation of Category 4 Breast Nodules. Chinese Journal of Ultrasound in Medicine, 39(07): 750–754.

He X, 2023, Research on Key Technology of Automatic 3D Imaging Report Generation Based on Conventional Breast Ultrasound, thesis, Chinese PLA Medical School.

Liu R, Yuan W, Liu W, 2022, Application of Ultrasonic Diagnostic System Based on AI Deep Learning Algorithm in Benign and Malignant Differentiation of Non-Palpable Breast Nodules. Journal of Zhengzhou University (Medical Sciences).