Automated CT image prescription of the gallbladder using deep learning: Development, evaluation, and health promotion

Most previous research on AI-based image diagnosis of acute cholecystitis (AC) has utilized ultrasound images. While these studies have shown promising outcomes, the results were based on still images captured by physicians, introducing inevitable selection bias. This study aims to develop a fully automated system for precise gallbladder detection among various abdominal structures, aiding clinicians in the rapid assessment of AC requiring cholecystectomy.

The dataset comprised images from 250 AC patients and 270 control participants. The VGG-16 architecture was employed for gallbladder recognition. Post-processing techniques such as the flood fill algorithm and centroid calculation were integrated into the model. U-Net was utilized for segmentation and features extraction. All models were combined to develop a fully automated AC detection system.

The gallbladder identification accuracy among various abdominal organs was 95.3%, with the model effectively filtering out CT images lacking a gallbladder. In diagnosing AC, the model was tested on 120 cases, achieving an accuracy of 92.5%, sensitivity of 90.4%, and specificity of 94.1%. After integrating all components, the ensemble model achieved an overall accuracy of 86.7%. The automated process required 0.029 seconds of computation time per CT slice and 3.59 seconds per complete CT set.

The proposed system achieves promising performance in the automatic detection and diagnosis of gallbladder conditions in patients requiring cholecystectomy, with robust accuracy and computational efficiency. With further clinical validation, this computer-assisted system could serve as an auxiliary tool in identifying patients requiring emergency surgery.