ArticleScientific reports2022
An accurate deep learning model for wheezing in children using real world data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Chronic obstructive pulmonary disease screening using time-frequency features of self-recorded respiratory sounds.JAMIA open · 2025Article
- A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.Scientific reports · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- Machine learning-driven strategies for enhanced pediatric wheezing detection.Frontiers in pediatrics · 2025Article
- Fused Audio Instance and Representation for Respiratory Disease Detection.Sensors (Basel, Switzerland) · 2024Article
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.Biosensors · 2024Article
- The Chinese Clinical Sleep Database: An Innovative Database System Includes Large-Scale Clinical Data of Chinese Population.Nature and science of sleep · 2024Article
- StethAid: A Digital Auscultation Platform for Pediatrics.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models require performance improvements and do not reflect the actual clinical situation. We aimed to develop an improved deep-learning model learning to detect wheezing in children, based on data from real clinical practice. In this prospective study, pediatric pulmonologists recorded and verified respiratory sounds in 76 pediatric patients who visited a university hospital in South Korea. In addition, structured data, such as sex, age, and auscultation location, were collected. Using our dataset, we implemented an optimal model by transforming it based on the convolutional neural network model. Finally, we proposed a model using a 34-layer residual network with the convolutional block attention module for audio data and multilayer perceptron layers for tabular data. The proposed model had an accuracy of 91.2%, area under the curve of 89.1%, precision of 94.4%, recall of 81%, and F1-score of 87.2%. The deep-learning model proposed had a high accuracy for detecting wheeze sounds. This high-performance model will be helpful for the accurate diagnosis of respiratory diseases in actual clinical practice.
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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.