Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

Figure 1 AI-SONIC breast system automatically recognizes markers and quantifies breast nodule characteristics. BI-RADS 4C, breast invasive ductal carcinoma confirmed by pathological findings. A: Conventional ultrasound BI-RADS classification suggests BI-RADS 4C; B: Automatic measurement and display of the growth direction; C: Edge feature analysis: the color changes from blue, green, yellow and red in turn to clear to blur; D: The dotted red line represents a strong echo; E: Based on the longitudinal section of the right breast nodule, the benign and. malignancy probability of this lesion was 0.84, as detected by artificial intelligence; F: The pathological diagnosis was invasive ductal carcinoma of the breast.

Figure 2 AI-SONIC breast system automatically recognizes markers and quantifies breast nodule characteristics. BI-RADS 4A, breast fibroadenoma confirmed by pathological findings. A: Conventional ultrasound BI-RADS classification suggests BI-RADS 4A; B: Automatic measurement and display of the growth direction; C: Edge feature analysis: the color changes from blue, green, yellow and red in turn to clear to blur; D: The dotted red line represents a strong echo; E: Based on the longitudinal section of the right breast nodule, the benign and malignancy probability of this lesion was 0.39, as detected by artificial intelligence; F: The pathological diagnosis was fibroadenoma of the breast.