Assessment of Cardiomegaly from Chest X-Ray Using Deep Learning Artificial Intelligence
Issued Date
2026-01-01
Resource Type
Scopus ID
2-s2.0-105036835996
Journal Title
Kst 2026 18th International Conference on Knowledge and Smart Technology
Start Page
98
End Page
103
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SCOPUS
Bibliographic Citation
Kst 2026 18th International Conference on Knowledge and Smart Technology (2026) , 98-103
Suggested Citation
Thongyoo J., Intagorn S., Borwarnginn P., Siriapisith T., Kusakunniran W. Assessment of Cardiomegaly from Chest X-Ray Using Deep Learning Artificial Intelligence. Kst 2026 18th International Conference on Knowledge and Smart Technology (2026) , 98-103. 103. doi:10.1109/KST67832.2026.11432387 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116494
Title
Assessment of Cardiomegaly from Chest X-Ray Using Deep Learning Artificial Intelligence
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Corresponding Author(s)
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Abstract
Chest radiography is essential in assessing heart size and shape, contributing to the screening of lung and heart diseases. The cardiothoracic ratio (CTR), defined as the ratio of the heart's largest transverse dimension to the chest's largest transverse dimension, serves as a crucial indicator of cardiac abnormalities. Traditional CTR measurement relies on segmentation-based approaches, which are prone to inaccuracies due to anatomical variations and inconsistencies in image quality. This study introduces a novel regression-based deep learning approach for automated CTR prediction, leveraging ProGAN-generated synthetic chest X-ray data. The proposed method eliminates the dependency on segmentation while enhancing accuracy and efficiency. A CNN model is developed and trained on ProGAN-generated images. The effectiveness of the regression approach is compared with conventional segmentationbased techniques, with accuracy assessments and Grad-CAM visualizations. The findings demonstrate improved performance and robustness in CTR prediction, offering a potential screening tool to assist medical professionals in assessing cardiomegaly.