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Background Lung sound analysis parameters have been reported to be useful biomarkers for evaluating airway condition. We developed an automatic lung sound analysis software program for infants and children based on lung sound spectral curves of frequency and power by leveraging machine learning (ML) technology. Methods To put this software program into clinical practice, in Study 1, the reliability and reproducibility of the software program using data from younger children were examined. In Study 2, the relationship between lung sound parameters and respiratory flow (L/s) was evaluated using data from older children. In Study 3, we conducted a survey using the ATS-DLD questionnaire to evaluate the clinical usefulness. The survey focused on the history of wheezing and allergies, among healthy 3-year-old infants, and then measured lung sounds. The clinical usefulness was evaluated by comparing the questionnaire results with the results of the new lung sound parameters. Results In Studies 1 and 2, the parameters of the new software program demonstrated excellent reproducibility and reliability, and were not affected by airflow (L/s). In Study 3, infants with a history of wheezing showed lower FAP 0 and RPF 75p ( p < 0.001 and p = 0.025, respectively) and higher PAP 0 ( p = 0.001) than healthy infants. Furthermore, infants with asthma/asthma-like bronchitis showed lower FAP 0 ( p = 0.002) and higher PAP 0 ( p = 0.001) than healthy infants. Conclusions Lung sound parameters obtained using the ML algorithm were able to accurately assess the respiratory condition of infants. These parameters are useful for the early detection and intervention of childhood asthma.
Background Lung sound analysis parameters have been reported to be useful biomarkers for evaluating airway condition. We developed an automatic lung sound analysis software program for infants and children based on lung sound spectral curves of frequency and power by leveraging machine learning (ML) technology. Methods To put this software program into clinical practice, in Study 1, the reliability and reproducibility of the software program using data from younger children were examined. In Study 2, the relationship between lung sound parameters and respiratory flow (L/s) was evaluated using data from older children. In Study 3, we conducted a survey using the ATS-DLD questionnaire to evaluate the clinical usefulness. The survey focused on the history of wheezing and allergies, among healthy 3-year-old infants, and then measured lung sounds. The clinical usefulness was evaluated by comparing the questionnaire results with the results of the new lung sound parameters. Results In Studies 1 and 2, the parameters of the new software program demonstrated excellent reproducibility and reliability, and were not affected by airflow (L/s). In Study 3, infants with a history of wheezing showed lower FAP 0 and RPF 75p ( p < 0.001 and p = 0.025, respectively) and higher PAP 0 ( p = 0.001) than healthy infants. Furthermore, infants with asthma/asthma-like bronchitis showed lower FAP 0 ( p = 0.002) and higher PAP 0 ( p = 0.001) than healthy infants. Conclusions Lung sound parameters obtained using the ML algorithm were able to accurately assess the respiratory condition of infants. These parameters are useful for the early detection and intervention of childhood asthma.
The assessment of auscultation using a stethoscope is unsuitable for continuous monitoring. Therefore, we developed a novel acoustic monitoring system that continuously, objectively, and visually evaluates respiratory sounds. In this report, we assess the usefulness of our revised system in a ventilated extremely low birth weight infant (ELBWI) for the diagnosis of pulmonary atelectasis and evaluation of treatment by lung lavage.A female infant was born at 24 weeks of age with a birth weight of 636 g after emergency cesarean section. The patient received invasive mechanical ventilation immediately after birth in our neonatal (NICU). After obtaining informed consent, we monitored her respiratory status using the respiratory-sound monitoring system by attaching a sound collection sensor to the right anterior chest wall. On day 26, lung-sound spectrograms showed that the breath sounds were attenuated simultaneously as hypoxemia progressed. Finally, chest radiography confirmed the diagnosis as pulmonary atelectasis. To relieve atelectasis, surfactant lavage was performed, after which the lung-sound spectrograms returned to normal. Hypoxemia and chest radiographic findings improved significantly. On day 138, the patient was discharged from the NICU without complications. The continuous respiratory-sound monitoring system enabled the visual, quantitative, and noninvasive detection of acute regional lung abnormalities at the bedside. We, therefore, believe that this system can resolve several problems associated with neonatal respiratory management and save lives.
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