Lung sound analysis in infants with risk factors for asthma development

Abstract

Background and objectives Using a lung sound analysis, the prognosis of asthma was investigated in infants with risk factors for asthma development by a long‐term observation. Methods A total of 268 infants were included (median age: 8 months old). The lung sound parameters (the ratio of the third and fourth area to the total area under the curve [A 3 /A T and B 4 /A T ], and the ratio of power and frequency at 50% and 75% of the highest frequency [RPF 50 and RPF 75 ]) were evaluated at the first visit. At 3 years old, using a questionnaire, we examined the relationship between the lung sound parameters and risk factors of asthma development. Results Among the 268 infants, 175 infants were in good health and 93 had a history of acute respiratory infection (ARI) within 7 days at the first visit. Among the 3‐ to 12‐month‐old infants with an ARI, the A 3 /A T , B 4 /A T values in those with a history of asthma/asthmatic bronchitis, atopic dermatitis, and atopy were smaller than in the infants without such histories. Among the 13‐ to 24‐month‐old infants with an ARI, the A 3 /A T and B 4 /A T values in those with a wheezing history were larger than in the infants without such a history. Conclusions The characteristics of the lung sounds in infants with risk factors for asthma development were demonstrated over long‐term follow‐up. Lung sound analyses may be useful for assessing the airway condition of infants.

Effects of COVID-19 pandemic–associated reduction in respiratory infections on infantile asthma development

“…Using the methods of previous studies, 7 , 11 , 12 a questionnaire survey was conducted on the same subjects at 18 months and 3 years of age. Using the American Thoracic Society–Division of Lung Diseases (ATS-DLD) questionnaire survey form (Japanese version; see Table E1 in this article’s Online Repository at www.jaci-global.org ), 7 the first questionnaire survey was conducted after obtaining informed consent from parents at the 18-month-old infant health checkup.…”

A review on lung disease recognition by acoustic signal analysis with deep learning networks

“…A 2011 review [62] emphasizes that previous studies can identify signs like wheezes or crackles. As earlier declared, the performance of classification and sound detection has significantly increased with the advent of deep and machine learning [42,43], and research about lung sound analysis has benefited from this development [65,110,150]. Lung sound analysis may be converted into a classification problem [29] with the help of identified markers, which is a problem class that machine learning excels at resolving.…”