ArticleIEEE access : practical innovations, open solutions2021
Exploring the Use of Artificial Intelligence Techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- The Effects of COVID-19 on Voice.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Generative AI and unstructured audio data for precision public health.npj health systems · 2025Article
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.PloS one · 2025Article
- Post-pandemic era: global trends, benefits, and barriers in integrating artificial intelligence into public health education.Frontiers in public health · 2025Review
- Omicron detection with large language models and YouTube audio data.medRxiv : the preprint server for health sciences · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
- Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.Sensors (Basel, Switzerland) · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.Sensors (Basel, Switzerland) · 2023Article
- COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence.Frontiers in public health · 2023Article
- Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms.IEEE journal of translational engineering in health and medicine · 2023Article
- Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.IEEE journal of biomedical and health informatics · 2022Article
- Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.Computers in biology and medicine · 2022Article
- CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications.Journal of ambient intelligence and humanized computing · 2022Article
- Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.
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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.