ORIGINAL RESEARCH article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
This article is part of the Research TopicEmerging Fast Medical Imaging Techniques in RadiologyView all 8 articles
Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases
Provisionally accepted
Chiharu Kai1
Satoshi Kasai1,2
Rei Teramoto1
Akifumi Yoshida2
Hideaki Tamori1,3Satoshi Kondo4Phan Thanh Hai5Nguyen Van Cong5
Dinh Minh Tuan5Thai Van Loc6,7
Naoki Kodama2*- 1Fujita Health University, Toyoake, Japan
- 2Niigata University of Health and Welfare, Niigata, Japan
- 3The Asahi Shimbun Company, Chuo, Japan
- 4Muroran Institute of Technology, Muroran, Japan
- 5MEDIC MEDICAL CENTER, Ho Chi Minh, Vietnam
- 6MEDICEN. Co. Ltd., Ho Chi Minh, Japan
- 7MEDIC MEDICAL CENTER, Ho Chi Minh, Japan
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Abstract Introduction: Vietnam still faces a high burden of infectious diseases compared with developed countries, and improving its health and sanitation environment is essential for addressing both infectious and non-communicable diseases. Chest radiography is key for early detection of cardiopulmonary diseases. Artificial Intelligence (AI) research on detecting cardiopulmonary diseases from chest radiographs has advanced; however, no AI development studies have used Vietnamese data, despite its high burden of both disease types, for early detection. Therefore, we aimed to develop an AI model to classify normal and abnormal images using a Vietnamese chest radiograph dataset. Methods: We retrospectively analyzed 12,827 normal and 4,644 abnormal cases from two Vietnamese institutions. Features were derived from principal component analysis and extracted using Vision Transformer and EfficientnetV2. We performed binary classification of normal and abnormal images using Light Gradient Boosting Machine with 5-fold cross-validation. Results: The model achieved an F1-score of 0.655, sensitivity of 0.596, specificity of 0.931, accuracy of 0.842, and AUC of 0.897. Subgroup evaluation revealed high accuracy in both infectious and non-communicable cases, as well as in urgent cases. Conclusion: We developed an AI system that classifies normal and abnormal chest radiographs with high clinical accuracy using Vietnamese data.
Keywords: Chest radiographs, artificial intelligence, vision Transformer, infectiousdiseases, Cardiopulmonary diseases
Received: 16 Sep 2025; Accepted: 05 Nov 2025.
Copyright: © 2025 Kai, Kasai, Teramoto, Yoshida, Tamori, Kondo, Hai, Cong, Tuan, Loc and Kodama. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Naoki Kodama, kodama@nuhw.ac.jp
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