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

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 EfficientNet. The IA model utilising DenseNet169 demonstrated 79.5% accuracy (76.4-82.6%), 70.1% sensitivity (64.7-75.0%), 87.6% specificity (83.7-90.7%), and an area under the curve (AUC) of 0.865 (0.862-0.867), with a negative appendectomy rate of 12.4% in stage 1 classification identifying non-appendicitis versus. appendicitis. In stage 2, the IA model exhibited 76.1% accuracy (70.3-81.9%), 82.6% sensitivity (62.9-90.9%), 74.2% specificity (67.0-80.3%), and an AUC of 0.827 (0.820-0.833), differentiating simple and complicated appendicitis. This IA model can provide physicians with reliable diagnostic information on appendicitis with generality and reproducibility within the VOI.