Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

Tran-Anh, Dat; Vu, Nam; Nguyen-Trong, Khanh; Pham, Cuong

doi:10.1016/j.pmcj.2022.101685

Cited by 7 publications

(1 citation statement)

References 28 publications

(30 reference statements)

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“…With the technical advances in computing, machine learning in deep planning models such as support vector machines (SVM), Random Forests, and neural networks have been utilized at an increasing pace to label and classify lung sound data [126]. The increasing fidelity and improvement in the performance of the resulting models could provide accurate diagnostic and predictive enrichment for specific disease states, such as pneumonia, pleural effusions, consolidations, and airway diseases (rhonchi and wheezing), among others.…”

Section: Clinical and Scientific Relevancementioning

confidence: 99%

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Garcia-Mendez,

Lal,

Herasevich

et al. 2023

Bioengineering

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Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

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Section: Clinical and Scientific Relevancementioning

confidence: 99%