ArticleAllergy, asthma & immunology research2024
Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.
Article in Allergy, asthma & immunology research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- Classifying social and physical pain from multimodal physiological signals using machine learning.Scientific reports · 2025Article
- Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry.Diagnostics (Basel, Switzerland) · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Considerations for Developing Diagnostic Artificial Intelligence: Towards Real-World Application of an Asthma Detection Model.Allergy, asthma & immunology research · 2024Article
- Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability.Frontiers in psychiatry · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma.
methodsData from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV
resultsIn total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV
conclusionsArtificial intelligence-based models showed excellent performance in asthma prediction compared to using PC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.