Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters
Abstract Background Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. Methods In this mu...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Pulmonary Medicine |
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| Online Access: | https://doi.org/10.1186/s12890-024-02945-7 |
| _version_ | 1797233864690630656 |
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| author | XiaoLing Zou Yong Ren HaiLing Yang ManMan Zou Ping Meng LiYi Zhang MingJuan Gong WenWen Ding LanQing Han TianTuo Zhang |
| author_facet | XiaoLing Zou Yong Ren HaiLing Yang ManMan Zou Ping Meng LiYi Zhang MingJuan Gong WenWen Ding LanQing Han TianTuo Zhang |
| author_sort | XiaoLing Zou |
| collection | DOAJ |
| description | Abstract Background Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. Methods In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. Results The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. Conclusion The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging. |
| first_indexed | 2024-04-24T16:22:57Z |
| format | Article |
| id | doaj.art-f6489e472abe45308ac0316a19f33435 |
| institution | Directory Open Access Journal |
| issn | 1471-2466 |
| language | English |
| last_indexed | 2024-04-24T16:22:57Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj.art-f6489e472abe45308ac0316a19f334352024-03-31T11:09:43ZengBMCBMC Pulmonary Medicine1471-24662024-03-0124111010.1186/s12890-024-02945-7Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parametersXiaoLing Zou0Yong Ren1HaiLing Yang2ManMan Zou3Ping Meng4LiYi Zhang5MingJuan Gong6WenWen Ding7LanQing Han8TianTuo Zhang9Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen UniversityScientific research project department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou)Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen UniversityDepartment of Pulmonary and Critical Care Medicine, Dongguan People’s HospitalDepartment of Pulmonary and Critical Care Medicine, the Six Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s HospitalDepartment of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen UniversityDepartment of Internal Medicine, Huazhou Hospital of Traditional Chinese MedicineDepartment of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen UniversityCenter for artificial intelligence in medicine, Research Institute of Tsinghua, Pearl River DeltaDepartment of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen UniversityAbstract Background Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. Methods In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. Results The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. Conclusion The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.https://doi.org/10.1186/s12890-024-02945-7COPD screeningPulmonary function testDeep learning modelsChest X-rayClinical parameters |
| spellingShingle | XiaoLing Zou Yong Ren HaiLing Yang ManMan Zou Ping Meng LiYi Zhang MingJuan Gong WenWen Ding LanQing Han TianTuo Zhang Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters BMC Pulmonary Medicine COPD screening Pulmonary function test Deep learning models Chest X-ray Clinical parameters |
| title | Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters |
| title_full | Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters |
| title_fullStr | Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters |
| title_full_unstemmed | Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters |
| title_short | Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters |
| title_sort | screening and staging of chronic obstructive pulmonary disease with deep learning based on chest x ray images and clinical parameters |
| topic | COPD screening Pulmonary function test Deep learning models Chest X-ray Clinical parameters |
| url | https://doi.org/10.1186/s12890-024-02945-7 |
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