AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images
10
Issued Date
2024-05-01
Resource Type
eISSN
25396056
Scopus ID
2-s2.0-85191459690
Journal Title
Journal of Associated Medical Sciences
Volume
57
Issue
2
Start Page
149
End Page
156
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Associated Medical Sciences Vol.57 No.2 (2024) , 149-156
Suggested Citation
Pamarapa C., Kemlek S., Sukumwattana W., Sitthikul P., Khuanrubsuan S., Chaikhampa A., Wongtrakool P., Chuajak A., Phonlakrai M., Keerativittayayut R. AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images. Journal of Associated Medical Sciences Vol.57 No.2 (2024) , 149-156. 156. doi:10.12982/JAMS.2024.037 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/98220
Title
AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images
Author's Affiliation
Corresponding Author(s)
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Abstract
Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection.