ArticleIEEE transactions on services computing2022
A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels.
Article in IEEE transactions on services computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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37 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.Journal of medical systems · 2025Article
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN.Health information science and systems · 2024Article
- Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker.Scientific reports · 2024Article
- Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study.Journal of medical Internet research · 2024Observational
- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.PloS one · 2024Article
- Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection.Scientific data · 2023Article
- Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction.Sensors (Basel, Switzerland) · 2023Article
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- Development and Validation of a Respiratory-Responsive Vocal Biomarker-Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19.Journal of medical Internet research · 2023Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.Scientific reports · 2023Article
- A summary of the ComParE COVID-19 challenges.Frontiers in digital health · 2023Article
- Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.Frontiers in artificial intelligence · 2023Article
- Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.Computers in biology and medicine · 2022Article
- Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.Journal of medical Internet research · 2022Article
- Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from
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