Objective ·To develop a multimodal prediction model that integrates endoscopic wound images with clinical features, leveraging deep learning and machine learning techniques to predict the risk of delayed post-polypectomy bleeding (DPPB). Methods ·The clinical data and postoperative endoscopic wound images of patients who underwent endoscopic colorectal polypectomy at two hospitals were retrospectively collected. The study was designed in three stages. In the first stage, a YOLOv11 model trained via transfer learning was used to automatically detect and segment wound areas, extracting regions of interest (ROI). In the second stage, ROI images were input into a deep neural network based on the ResNet50 architecture. Supervised learning was performed using DPPB occurrence as the label, and a standardized endoscopic imaging score (E-Score) was generated. In the third stage, the E-Score was combined with clinical features. Feature selection was conducted using LASSO regression, and the selected variables were input into multiple machine learning algorithms to construct a multimodal DPPB prediction model. Finally, a web-based application was developed using the Streamlit framework to facilitate clinical use. Results ·A total of 2 782 patients who underwent colorectal polypectomy were included, among whom 228 (8.20%) developed DPPB. In the test set, the multimodal prediction model built using the XGBoost algorithm (PrismDPPB) achieved the best performance, with an area under the curve(AUC) of 0.831 (95%CI 0.81‒0.85), outperforming other models. It also showed superior performance in accuracy (79.78%), sensitivity (77.28%), positive predictive value (81.32%), and F1 score (79.25%). Feature importance analysis revealed that the six most contributive variables were E-Score, maximum polyp base diameter, sex, presence of a stalk, body mass index, and polyp location. For model interpretability, SHAP plots were used to visualize and explain the prediction results. Conclusion ·The multimodal prediction model developed by integrating image-based features with clinical characteristics, along with the accompanying web application, demonstrates strong practicality and clinical potential for DPPB risk assessment.