There were 101 incorrectly identified sections in 2362 slides, including 42 false negatives and 59 false positives. Through NVivo 12 Plus, qualitative analysis of word frequency analysis and nodal analysis was performed on the error reasons, and the top-down error reason framework of “artificial intelligence RRCART model to identify frozen sections of breast cancer lymph nodes” was constructed based on the importance of false reasons. The pathologists and artificial intelligence engineers jointly read the images and heatmaps to determine the locations of the identified errors on sections, and the pathologists found the reasons (false reasons) for the errors. We took the pathological diagnosis of senior pathologists as the gold standard and identified errors. The purpose is to summarize the factors that affect the accuracy of the artificial intelligence model and propose corresponding solutions. We propose an RRCART model based on a deep-learning network to identify metastases in 2362 frozen sections and count the wrongly identified sections and the associated reasons. Intraoperative frozen section of sentinel lymph nodes is an important basis for determining whether axillary lymph node dissection is required for breast cancer surgery. Breast cancer is the most common malignant tumor in the world.

Shotty metastatic lymph nodes