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Volume 15
Issue 4
10.3390/diagnostics15040449
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Article

Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry

by
SangJee Park
1,†,
Yehyeon Yi
2,†,
Seon-Sook Han
3,
Tae-Hoon Kim
3,
So Jeong Kim
4,
Young Soon Yoon
5,
Suhyun Kim
2,
Hyo Jin Lee
6 and
Yeonjeong Heo
3,*
1
Biomedical Research Institute, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea
2
Department of Internal Medicine, Seoul Medical Center, Seoul 02053, Republic of Korea
3
Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea
4
Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong 18450, Republic of Korea
5
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang 10326, Republic of Korea
6
Internal Medicine, Seoul National University Seoul Metropolitan Government Boramae Medical Center, Seoul 07061, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2025, 15(4), 449; https://doi.org/10.3390/diagnostics15040449
Submission received: 18 December 2024 / Revised: 31 January 2025 / Accepted: 8 February 2025 / Published: 12 February 2025
(This article belongs to the Special Issue AI-Driven Diagnostics: Transforming Healthcare from Data to Clinical Decisions)
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Abstract

Background/Objectives: The methacholine bronchial provocation test (MBPT) is a diagnostic test frequently used to evaluate airway hyper-reactivity. MBPT is essential for diagnosing asthma; however, it can be time-consuming and resource-intensive. This study aimed to develop an artificial intelligence (AI) model to predict the MBPT results using forced expiratory volume in one second (FEV1) and bronchodilator test measurements from spirometry. Methods: a dataset of spirometry measurements, including Pre- and Post-bronchodilator FEV1, was used to train and validate the model. Results: Among the evaluated models, the multilayer perceptron (MLP) achieved the highest area under the curve (AUC) of 0.701 (95% CI: 0.676–0.725), accuracy of 0.758, and an F1-score of 0.853. Logistic regression (LR) and a support vector machine (SVM) demonstrated comparable performance with AUC values of 0.688, while random forest (RF) and extreme gradient boost (XGBoost) achieved slightly lower AUC values of 0.669 and 0.672, respectively. Feature importance analysis of the MLP model identified key contributing features, including Pre-FEF25–75 (%), Pre-FVC (L), Post FEV1/FVC, Change-FEV1 (L), and Change-FEF25–75 (%), providing insight into the interpretability and clinical applicability of the model. Conclusions: These results highlight the potential of the model to utilize readily available spirometry data, particularly FEV1 and bronchodilator responses, to accurately predict MBPT results. Our findings suggest that AI-based prediction can improve asthma diagnostic workflows by minimizing the reliance on MBPT and enabling faster and more accessible assessments.
Keywords: methacholine bronchial provocation test; machine learning; asthma methacholine bronchial provocation test; machine learning; asthma

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MDPI and ACS Style

Park, S.; Yi, Y.; Han, S.-S.; Kim, T.-H.; Kim, S.J.; Yoon, Y.S.; Kim, S.; Lee, H.J.; Heo, Y. Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry. Diagnostics 2025, 15, 449. https://doi.org/10.3390/diagnostics15040449

AMA Style

Park S, Yi Y, Han S-S, Kim T-H, Kim SJ, Yoon YS, Kim S, Lee HJ, Heo Y. Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry. Diagnostics. 2025; 15(4):449. https://doi.org/10.3390/diagnostics15040449

Chicago/Turabian Style

Park, SangJee, Yehyeon Yi, Seon-Sook Han, Tae-Hoon Kim, So Jeong Kim, Young Soon Yoon, Suhyun Kim, Hyo Jin Lee, and Yeonjeong Heo. 2025. "Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry" Diagnostics 15, no. 4: 449. https://doi.org/10.3390/diagnostics15040449

APA Style

Park, S., Yi, Y., Han, S.-S., Kim, T.-H., Kim, S. J., Yoon, Y. S., Kim, S., Lee, H. J., & Heo, Y. (2025). Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry. Diagnostics, 15(4), 449. https://doi.org/10.3390/diagnostics15040449

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