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

作者：Chongxiang Chen, Felix Herth, Yingnan Zuo, Hongjia Li, Xinyuan Liang, Yaqing Chen, Jiangtao Ren, Wenhua Jian, Changhao Zhong, Shiyue Li · 发表于：Therapeutic Advances in Chronic Disease · 年份：2023 · DOI：10.1177/20406223231181495 · 被引用次数：16 · 研究领域：Lung Cancer Diagnosis and Treatment、COVID-19 diagnosis using AI、Advanced Radiotherapy Techniques

Background: Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy. Objectives: To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods: Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results: In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear...