Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images

被引：2

作者：

Chen, Chongxiang ^{[1
]}

Tang, Fei ^{[2
,3
]}

Herth, Felix J. F. ^{[4
,5
]}

Zuo, Yingnan ^{[6
]}

Ren, Jiangtao ^{[7
]}

Zhang, Shuaiqi ^{[8
]}

Jian, Wenhua ^{[1
]}

Tang, Chunli ^{[1
]}

Li, Shiyue ^{[1
]}

机构：

[1] Guangzhou Med Univ, Guangzhou Inst Resp Hlth, Natl Clin Res Ctr Resp Dis, State Key Lab Resp Dis,Affiliated Hosp 1, Guangzhou 510000, Guangdong, Peoples R China

[2] Anhui Chest Hosp, Dept Intervent Pulm & Endoscop Diag, Hefei, Anhui, Peoples R China

[3] Anhui Chest Hosp, Treatment Ctr, Hefei, Anhui, Peoples R China

[4] Univ Hosp Heidelberg, Dept Pneumol & Crit Care Med, Heidelberg, Germany

[5] Univ Hosp Heidelberg, Translat Res Unit, Thoraxklin, Heidelberg, Germany

[6] Guangzhou Tianpeng Comp Technol Co Ltd, Guangzhou, Guangdong, Peoples R China

[7] Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangdong Prov Key Lab Computat Sci, Guangzhou, Guangdong, Peoples R China

[8] Sun Yat Sen Univ, Sch Publ Hlth, Guangzhou, Peoples R China

来源：

THERAPEUTIC ADVANCES IN RESPIRATORY DISEASE | 2024年 / 18卷

关键词：

artificial intelligence; bronchoscopy; tracheobronchopathia osteochondroplastica; TO; ENDOSCOPY;

D O I：

10.1177/17534666241253694

中图分类号：

R56 [呼吸系及胸部疾病];

学科分类号：

摘要：

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.

引用

页数：11