Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks.

ABSTRACT: This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n

SUBMITTER: Liang H 

PROVIDER: S-EPMC10579320 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature