Science progress distinguishing different types of airway stents under bronchoscopy by artificial intelligence

Objective In prior research, we employed artificial intelligence (AI) to distinguish different anatomical positions in the airway under bronchoscopy. We aimed to leverage AI to identifying different types of airway stent. Methods To “deep learn” imaging data from patients who underwent bronchoscopy for implanting airway stents from January 2010 to June 2024, utilizing the Vision Transformer model (AI architecture). Eight percent of randomized clear images of the upper ends of stents from 662 patients were used to train for three main types of airway stent (T-shaped silicone, silicone, and metal-covered), and to determine if the stents were Y-shaped. The remaining 20% of clear images were utilized for validation. Results A total of 1254 bronchoscopic images of the upper ends and interiors of stents from 662 patients with different types of stents were analyzed. These types of stents were T-shaped silicone (70 patients), Y-shaped silicone stents (121), non-Y-shaped silicone stents (196), Y-shaped metal covered (67), and non-Y-shaped metal covered (208). A total of 662 bronchoscopic images depicting the upper ends of stents were utilized to identify three primary types of stents: T-shaped silicone, all silicone, and all metal covered. The mean accuracy for recognizing these three types was 98.5%, with individual accuracies of 93.3% for T-shaped silicone, 98.4% for all silicone, and 100% for all metal-covered stents. The area under the curve value for these three types was >0.99. Additionally, 592 images of stent interiors were employed for training and validation to determine if they were Y-shaped, and if they could be categorized further into Y-shaped silicone, non-Y-shaped silicone, Y-shaped metal-covered, or non-Y-shaped metal-covered stents. The accuracies for identifying Y-shaped silicone stents and Y-shaped metal-covered stents were 95.5% and 100%, respectively. Conclusions Artificial intelligence technology can differentiate between various types of stent utilizing bronchoscopy images. The trained model holds potential to improve quality control in future clinical applications.