Development and validation of automated three-dimensional convolutional neural network model for acute appendicitis diagnosis.

ABSTRACT: Rapid, accurate preoperative imaging diagnostics of appendicitis are critical in surgical decisions of emergency care. This study developed a fully automated diagnostic framework using a 3D convolutional neural network (CNN) to identify appendicitis and clinical information from patients with abdominal pain, including contrast-enhanced abdominopelvic computed tomography images. A deep learning model-Information of Appendix (IA)-was developed, and the volume of interest (VOI) region corresponding to the anatomical location of the appendix was automatically extracted. It was analysed using a two-stage binary algorithm with transfer learning. The algorithm predicted three categories: non-, simple, and complicated appendicitis. The 3D-CNN architecture incorporated ResNet, DenseNet, and Efficie

SUBMITTER: Kim M 

PROVIDER: S-EPMC11882796 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature