Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.

尽管一直被证明可以检测出乳房 X 线照相术中隐匿的癌症，但乳房超声已被注意到具有很高的假阳性率。在这项工作中，我们提出了一个 AI 系统，可以在超声图像中识别乳腺癌方面达到放射科医师级别的准确性。该 AI 基于 288,767 次检查开发，包括 5,442,907 张 B 模式和彩色多普勒图像，在包含 44,755 次检查的测试集上实现了 0.976 的接受者操作特征曲线下面积 (AUROC)。在一项回顾性读者研究中，AI 获得的 AUROC 高于十名获得委员会认证的乳腺放射科医生的平均值（AUROC：0.962 AI，0.924 ± 0.02 放射科医生）。在 AI 的帮助下，放射科医生将他们的假阳性率降低了 37.3%，并将要求的活检减少了 27.8%，同时保持了相同的灵敏度水平。