Fig. 1: Overview of the system’s pipeline.

a US images were pre-processed to extract the breast laterality (i.e., left or right breast) and to include only the part of the image which shows the breast (cropping out the image periphery which typically contains textual metadeta about the patient and US acquisition technique). b For each breast, we assigned a cancer label using the recorded pathology reports for the respective patient within −30 and 120 days from the time of the US examination. We applied additional filtering on the internal test set to ensure that cancers in positive exams are visible in the US images and negative exams have at least one cancer-negative follow-up (see Methods section `Additional filtering of the test'). c The AI system processes all US images acquired from one breast to compute probabilistic predictions for the presence of malignant lesions. The AI system also generates saliency maps that indicate the informative regions in each image. d We evaluated the system on an internal test set (AUROC: 0.976, 95% CI: 0.972, 0.980, n = 79,156 breasts) and an external test set (AUROC: 0.927, 95% CI: 0.907, 0.959, n = 780 images). e In a reader study consisting of 663 exams (n = 1024 breasts), we showed that the AI system can improve the specificity and positive predictive value (PPV) for 10 attending radiologists while maintaining the same level of sensitivity and negative predictive value (NPV).