Evidence map›Paper›PMID 40358760›Full record

ArticleEuropean journal of pediatrics2025

Characteristics of lung sounds in early infants using automated analysis.

Yoshifumi Murayama, Hiroyuki Mochizuki, Hidetoshi Yano, Shigeki Ochiai, Mayumi Enseki, Takashi Koike, Hiroyuki Furuya, Yoshiyuki Yamada, Atsushi Uchiyama

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Article in European journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yoshifumi MurayamaDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Hiroyuki MochizukiDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Hidetoshi YanoDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Shigeki OchiaiDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Mayumi EnsekiDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Takashi KoikeDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Hiroyuki FuruyaDepartment of Basic Clinical Science and Public Health, Tokai University School of Medicine, Isehara, Japan.
Yoshiyuki YamadaDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan.
Atsushi UchiyamaDepartment of Paediatrics, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259 - 1193, Japan. a.uchiyama@tokai.ac.jp.ORCID http://orcid.org/0009-0005-5712-5757

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new lung sound analysis software program has been developed. It can automatically select a typical lung sound spectrogram and calculate lung sound parameters using machine learning programs. This study aimed to clarify lung sound characteristics in early infants using this program. Using the program, the characteristics of lung sounds in healthy 1- and 4-month-old infants were examined. The lung sounds were assessed in the supine position for 1-month-old infants and in both the sitting and supine positions for 4-month-old infants. We compared the characteristics of the infant lung sounds with those of healthy 3-year-old children. The lung sound parameters of the 1-month-old infants (n = 58) were affected by gender, height, and birth weight. However, those of the 4-month-old infants (n = 50) obtained in the sitting or supine position were not affected by these factors in the study. The lung sound parameters obtained in the sitting and supine positions were not significantly different, and they were not related to a history of wheezing or allergy. PAP

conclusionThe new program confirmed the specificity of lung sound parameters in early infants. Further studies are needed to clarify why the specificity differs from that of 3-year-old children. WHAT IS KNOWN: • Lung sound analysis has been evaluated to some extent as safe and straightforward for estimating lung function in toddlers and older humans. However, it has not been applied to early infants. WHAT IS NEW: • A lung sound analysis method using new analysis software has clarified the characteristics of lung spectra in early infants. This technique may facilitate the early diagnosis and treatment of respiratory diseases in infants.

Indexed as

Machine LearningRespiratory SoundsSoftwareChild, PreschoolFemaleHumansInfantMaleSound SpectrographySupine PositionArtificial intelligenceChronic lung diseaseInfantLung sound analysis

Identifiers

PMID40358760
PMCPMC12075377

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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.