Evidence map›Paper›PMID 38803144›Full record

ArticleTherapeutic advances in respiratory disease

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

Chongxiang Chen, Fei Tang, Felix J F Herth, Yingnan Zuo, Jiangtao Ren, Shuaiqi Zhang, Wenhua Jian, Chunli Tang, Shiyue Li

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicine
    Review
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chongxiang ChenState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China.ORCID 0000-0002-2079-881X
Fei TangDepartment of Interventional Pulmonary and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, Anhui Province, China.
Felix J F HerthDepartment of Pneumology and Critical Care Medicine and Translational Research Unit, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany.
Yingnan ZuoGuangzhou Tianpeng Computer Technology Co., Ltd. Guangzhou, Guangdong, China.
Jiangtao RenSchool of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong, China.
Shuaiqi ZhangSchool of Public Health, Sun Yat-sen University, Guangzhou, China.
Wenhua JianState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China.
Chunli TangState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China.
Shiyue LiState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings.

objectivesTo build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images.

designWe 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.

methodsBronchoscopic 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).

resultsWe 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%.

conclusionWe 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.

Indexed as

BronchoscopyOsteochondrodysplasiasPredictive Value of TestsTracheal DiseasesAdultAgedArtificial IntelligenceChinaDeep LearningDiagnosis, DifferentialFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeural Networks, Computerartificial intelligencebronchoscopyTOtracheobronchopathia osteochondroplastica

Identifiers

PMID38803144
PMCPMC11131396

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.