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Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests.
Kang, Noeul; Lee, KyungHyun; Byun, Sangwon; Lee, Jin-Young; Choi, Dong-Chull; Lee, Byung-Jae.
Afiliación
  • Kang N; Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Lee K; Department of Electronics Engineering, Incheon National University, Incheon, Korea.
  • Byun S; Department of Electronics Engineering, Incheon National University, Incheon, Korea.
  • Lee JY; Health Promotion Center, Samsung Medical Center, Seoul, Korea.
  • Choi DC; Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Lee BJ; Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. leebj@skku.edu.
Allergy Asthma Immunol Res ; 16(1): 42-54, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38262390
ABSTRACT

PURPOSE:

The 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.

METHODS:

Data 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 [FEV1]; PC20 ≤ 16 mg/mL) was compared with the prediction models derived from five ML

methods:

logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV1, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF25%-75%) values obtained during MCT.

RESULTS:

In 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 FEV1, FVC, and FEF25%-75% were included as inputs.

CONCLUSIONS:

Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC20 ≤ 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Allergy Asthma Immunol Res Año: 2024 Tipo del documento: Article Pais de publicación:
Texto completo
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Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Allergy Asthma Immunol Res Año: 2024 Tipo del documento: Article Pais de publicación: