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

Abstract

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 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.

Keywords

• Artificial intelligence
• Acute appendicitis
• Convolutional neural network