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目的:探究一种融合了SAM-YOLOV 5深度学习网络和图像处理技术的人工智能(AI)新模型在乳腺影像报告与数据系统(BI-RADS)4类肿块超声动态视频良恶性分类中的应用。方法:回顾性收集2019年5月至2023年6月汕头大学医学院第一附属医院经病理证实的BI-RADS 4类的乳腺肿块患者458例(530个肿块)，按7∶3的比例进行模型的训练和测试，分析模型的ROC曲线下面积(AUC)、敏感性、特异性、阳性预测值、阴性预测值。先与单张静态图像下的测试效果进行比较，再与3个传统的深度学习网络以及高、低年资医师组的测试效果进行比较。分析新模型在BI-RADS 4a、4b、4c类肿块中的诊断效能。结果:二维超声动态视频在新模型中测试所得到的AUC、敏感性、特异性、阳性预测值、阴性预测值高于使用单张超声静态图像(均 P<0.05)。基于二维超声动态视频下，新模型的AUC、敏感性、特异性、阳性预测值、阴性预测值高于3个深度学习网络模型(YOLOV 5、VGG 16、Resnet 50)和低年资医师组(均 P<0.05)，低于高年资医师组(其中仅特异性、阴性预测值 P<0.05)。新模型对BI-RADS 4b类肿块诊断效能最低。 结论:基于SAM-YOLOV 5深度学习网络和图像处理技术开发的用于BI-RADS 4类乳腺肿块动态超声分类诊断的新模型有较高的诊断价值，有望用于辅助临床诊断。

Objective:To investigate the diagnostic performance of a new artificial intelligence (AI) model incorporating SAM-YOLOV 5 deep learning network and image processing techniques for Breast Imaging Reporting and Data System (BI-RADS) 4 category breast masses in dynamic ultrasound classification.Methods:A total of 530 pathologically proven breast lesions of BI-RADS category 4 in 458 patients were retrospectively collected from May 2019 to June 2023 at the First Affiliated Hospital of Shantou University Medical College. The model was trained and tested at ratio of 7∶3, the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value of the model were determined. Firstly, the test results of the model were compared with a single static image, then, compared with the three conventional deep learning networks as well as senior and junior radiologists. The diagnostic efficiency of the new model in BI-RADS categories 4a, 4b, and 4c masses were analyzed.Results:The AUC, sensitivity, specificity, positive predictive value and negative predictive value of the new model based on dynamic ultrasound video were higher than those using a single ultrasound static imaging (all P<0.05). Based on dynamic ultrasound video, the AUC, sensitivity, specificity, positive predictive value and negative predictive value of the new model were significantly higher than those of YOLOV 5, VGG 16, Resnet 50 and the junior group (all P<0.05), lower than the senior group (just specificity and negative predictive value, P<0.05). The diagnostic efficiency of new model for BI-RADS category 4b masses was the lowest. Conclusions:Based on the SAM-YOLOV 5 deep learning network and image processing techniques, the new model has a high diagnostic value for breast mass dynamic ultrasound classification and is expected to be used in assisting clinical diagnosis.