The performance of 
MALIGNANCIES
We constructed a real‐time pancreatic mass capture and segmentation model (Model 1) under CH‐EUS, a benign and malignant identification model (Model 2), and an EUS‐FNA‐targeted auxiliary system. Model 1 successfully identified and segmented the pancreatic mass region of 155 images. Compared with manual labeling by experts, the average IoU (average overlap rate) of Model 1 was 0.708, and the accuracy rate was 87.8% under the overlap rate threshold of 0.50 in CH‐EUS images. Representative results for model prediction are shown in Figure Representative results for Model 1 prediction. In the diagram, the area outlined by the green dashed line is the expert's marked area and the area outlined by the red dashed line is the prediction result area of Model 1.In the testing set, Model 2 identified malignancies from benign lesions with an accuracy of 88.9%, a sensitivity of 100%, a specificity of 75%, a positive predictive value of 83.3%, and a negative predictive value of 100%.