Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification

Abstract

Background: Bronchoscopy is a key step in the diagnosis and treatment of respiratory diseases. However, the level of expertise varies among different bronchoscopists. Artificial intelligence (AI) may help them identify bronchial lumens. Thus, a bronchoscopy quality-control system based on AI was built to improve the performance of bronchoscopists.Methods: This single-center observational study consecutively collected bronchoscopy videos from Shanghai Chest Hospital and segmented each video into 31 different anatomical locations to develop an AI-assisted system based on a convolutional neural network (CNN) model. We then designed a single-center trial to compare the accuracy of lumen recognition by bronchoscopists with and without the assistance of the AI system.Results: A total of 28,441 qualified images of bronchial lumen were used to train the CNNs. In the crossvalidation set, the optimal accuracy of the six models was between 91.83% and 96.62%. In the test set, the visual geometry group 16 (VGG-16) achieved optimal performance with an accuracy of 91.88%, and an area under the curve of 0.995. In the clinical evaluation, the accuracy rate of the AI system alone was 54.30% (202/372). For the identification of bronchi except for segmental bronchi, the accuracy was 82.69% (129/156).

A combination of flexible and rigid bronchoscopy in the successful removal of a residual fish bone from a peripheral bronchus: A case report

“…In this golden age of rapid advances in artificial intelligence (AI), the combination of AI and medicine poses a profound effect on every aspect of healthcare ( 23 ) with significant progress in image classification, segmentation, and object detection ( 24 , 25 ), which shed light on the difficult FB management by bronchoscopy. For instance, Li et al built a bronchoscopy quality-control system based on AI and showed that the supplemental application of the AI system could reduce the differences in the endoscopic skills of doctors with different levels of experience ( 26 ). However, at present, only limited studies have been taken in the application of AI to bronchoscopy ( Supplementary Table S1 ) ( 26 – 31 ), most of which are mainly focused on recognizing tumors ( 27 , 28 , 31 ).…”

“…For instance, Li et al built a bronchoscopy quality-control system based on AI and showed that the supplemental application of the AI system could reduce the differences in the endoscopic skills of doctors with different levels of experience ( 26 ). However, at present, only limited studies have been taken in the application of AI to bronchoscopy ( Supplementary Table S1 ) ( 26 – 31 ), most of which are mainly focused on recognizing tumors ( 27 , 28 , 31 ). Particularly, there is a research gap in the application of AI in complicated FB aspiration.…”

Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network

“…The study conducted by Li et al 31 also recognizes the anatomical positions under bronchoscopy. However, there were several differences between our studies.…”

Artificial intelligence in interventional pulmonology

“…The accuracy for the determination of anatomical location was 38–51% in anesthesiologists and 68–82% in pulmonologists, whereas the pretrained CNN model showed an 86% accuracy. Li et al [24 ▪ ] reported bronchial tree anatomical identification using trained CNN models. They included images of not only the carina and both main bronchi, but also all segmental bronchi that are usually observed during bronchoscopy.…”