Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.

ObjectivesThis study aimed to develop a multimodal predictive model that integrates clinical data, radiomics, and three-dimensional deep learning to forecast acute respiratory distress syndrome in patients with acute pancreatitis.MethodsThis retrospective study analyzed data from 759 patients with acute pancreatitis treated at three hospitals. Radiomics features were extracted from three-dimensional computed tomography images, and a three-dimensional deep learning model was developed using convolutional networks. These components were combined with clinical data using the XGBoost algorithm to construct a multimodal model. The performance of the model was compared with that of single-modal models and traditional scoring systems (Modified Computed Tomography Severity Index, Ranson score, and Bedside Index for Severity in Acute Pancreatitis), using area under the curve as the primary metric. Model interpretability was enhanced using variable importance analysis, SHapley Additive exPlanations, local interpretable model-agnostic explanations, calibration plots, and decision curve analysis.ResultsThe multimodal model achieved area under the curve values of 0.872 (training set) and 0.876 (test set), outperforming traditional scores (Modified Computed Tomography Severity Index: 0.747 and 0.759; Ranson score: 0.575 and 0.568; and Bedside Index for Severity in Acute Pancreatitis: 0.748 and 0.757, respectively) and single-modal models (radiomics: 0.638 and 0.727 and deep learning: 0.756 and 0.727, respectively).ConclusionBy integrating clinical tabular data, radiomics, and deep learning features, the multimodal model can predict the risk of acute respiratory distress syndrome in patients with acute pancreatitis at an early stage.