Cited by Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

A comprehensive review of computerized respiratory sound analysis and deep learning techniques for acoustic signal-based disease classification DOI

E. Sandhya,

Bhavya Sri Kodipunjula,

Uday Kiran Appalaneni

et al.

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3281, P. 040035 - 040035

Published: Jan. 1, 2025

Language: Английский

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Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis DOI

Vivianne Landry, Jessica Matschek, Richard Pang

et al.

European Respiratory Review, Journal Year: 2025, Volume and Issue: 34(176), P. 240246 - 240246

Published: April 1, 2025

Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics monitoring. In respiratory care, evidence supporting their clinical use remains fragmented inconclusive. This study aimed to assess current research landscape digital medicine through a bibliometric analysis systematic review (PROSPERO CRD 42022336730). MEDLINE, Embase, Cochrane Library CINAHL were searched references indexed up 9 April 2024. Eligible studies evaluated accuracy sound diagnosing managing obstructive (asthma COPD) or infectious diseases, excluding COVID-19. A narrative synthesis was conducted, QUADAS-2 tool used quality risk bias. Of 14 180 studies, 81 included. Bibliometric identified fundamental ( e.g. “diagnostic accuracy”+“machine learning”) emerging “developing countries”) themes. Despite methodological heterogeneity, generally achieved moderate (60–79%) high (80–100%) accuracies. 80% (eight out ten) reported sensitivities specificities asthma diagnosis, 78% (seven nine) 56% (five COPD, 64% eleven) sensitivity specificity values pneumonia diagnosis. Breathing coughing most common biomarkers, with neural networks being technique. Future on should focus testing validity clinically diverse populations resolving algorithmic If successful, hold promise complementing existing tools enabling more accessible applications telemedicine, communicable monitoring, chronic condition management.

Language: Английский

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Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks DOI

Abdelkrim Semmad, Mohammed Bahoura

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 171, P. 108190 - 108190

Published: Feb. 20, 2024

Language: Английский

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