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Background: Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings., Objectives: To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images., Design: We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation., Methods: Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs)., Results: We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%., Conclusion: We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.

Objective: To evaluate the value of T-SPOT.TB in the diagnosis of tracheobronchial tuberculosis., Methods: This study included in January 2010 to October 2014 in the three gorges university during the first clinical medical college of final 283 diagnosed with tracheal bronchus TB patients (including 273 patients with pathological biopsy diagnosis and 10 called suspected TB patients), at the same time will be 283 active TB patients as a parallel control group included in this study. They were all given traditional detection methods acid fast stain and diseased tissue pathological biopsy and the new detection method T-SPOT.TB., Results: Sputum smear acid-fast stain sensitivity rate is 39.2% (111/283), typical TB diseased tissue pathology biopsy pathology morphology of 221 cases (78.1%), tend to TB 52 cases (18.4%),while T-SPOT. TB testing sensitivity and speciality rate is 93.6% (265/283) and 85.1% (241/283), which is much higher then the former two., Conclusion: T-SPOT.TB can provide important basis for the diagnosis of tracheobronchial tuberculosis.It is the fastest and most accu-rate method in the diagnosis of tracheobronchial tuberculosis.