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Updated: Apr 20, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Prediction of airway inflammation in patients with asymptomatic asthma by using lung sound analysis
Terufumi Shimoda1, Yukio Nagasaka2, Yasushi Obase3
1Clinical Research Center, Fukuoka National Hospital, Fukuoka, Japan.
Background:
The intensity and frequency of sounds in a lung sound analysis (LSA) may be related to airway constriction; however, whether any factors of an LSA can predict airway eosinophilic inflammation in patients with asthma is unknown.
Objective:
To determine whether an LSA can predict airway eosinophilic inflammation in patients with asymptomatic asthma.
Methods:
The expiratory-inspiratory ratios of sound power in the low-frequency range (E-I LF) from 36 patients with asymptomatic asthma were compared with those of 14 healthy controls. The relations of E-I LF with airway eosinophilic inflammation were analyzed. The E-I LF cutoff value for predicting airway eosinophilic inflammation also was analyzed.
Results:
The mean ± SD E-I LF was higher in the patients with asthma and with increased sputum eosinophils than in those patients without increased sputum eosinophils (0.45 ± 0.24 vs 0.20 ± 0.12; P < .001) or in the healthy controls (0.25 ± 0.10; P = .003). A multiple regression analysis showed that the sputum eosinophil ratio and exhaled nitric oxide were independently correlated with E-I LF, P = .0003 and P = .032, respectively. For the prediction of increased sputum eosinophils and increased fractional exhaled nitric oxide levels, the E-I LF thresholds of 0.29 and 0.30 showed sensitivities of 0.80 and 0.74 and specificities of 0.83 and 0.77, respectively.
Conclusions:
We showed that LSAs can safely predict airway inflammation of patients with asymptomatic asthma.
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