ArticlePeerJ. Computer science2022
A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- A device-invariant multi-modal learning framework for respiratory disease classification.NPJ digital medicine · 2026Article
- Automatic chick cough detection system based on improved audio spectrogram convolutional transformer neural network.Frontiers in veterinary science · 2026Article
- DETECTX: a novel deep learning approach for detecting pneumonia, tuberculosis, and aspergillosis from chest X-rays.Frontiers in cellular and infection microbiology · 2026Article
- Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection.NPJ digital medicine · 2025Article
- Update on Patient Self-Testing with Portable and Wearable Devices: Advantages and Limitations.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- A smartphone-based application for cough counting in patients with acute asthma exacerbation.Journal of thoracic disease · 2023Article
- Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.Frontiers in artificial intelligence · 2023Article
- Cough Audio Analysis for COVID-19 Diagnosis.SN computer science · 2023Article
- The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review.Frontiers in medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.
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Registered trials
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.