Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.

ABSTRACT: To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional Networks (ResNet + FCN) segmentation model were trained. The better effective model was selected to appraise the classification performance of 3 breast radiologists and 3 non-breast radiologists. The evaluation metrics included sensitivity, specificity, and positive predictive value (PPV). The ResNet101 model demonstrated superior performance compared to the ResNet + FCN model. It significantly enh

SUBMITTER: Yan H 

PROVIDER: S-EPMC11973185 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature