ArticleIEEE access : practical innovations, open solutions2021
Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.
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 28 papers, 3 of them syntheses that pooled it.
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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
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 133 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.Nursing & health sciences · 2025Pooled it
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification.Sensors (Basel, Switzerland) · 2026Article
- The Use of Artificial Intelligence in Improving Diagnostic Modalities in Rheumatoid Arthritis: A Narrative Review.Cureus · 2026Review
- Unmet need for biomarkers in chronic cough: a review of current challenges and future directions.ERJ open research · 2025Review
- Review
- Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish.BMC medical informatics and decision making · 2025Article
- Five-Cavity Resonance Inspired, rGO Nano-Sheet Reinforced, Multi-Site Voice Synergetic Detection Hydrogel Sensors with Diverse Self-Adhesion and Robust Wireless Transmissibility.Gels (Basel, Switzerland) · 2025Article
- Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events.NPJ digital medicine · 2025Article
- Digital markers of asthma exacerbations: a systematic review.ERJ open research · 2024Review
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems.Scientific reports · 2024Article
- Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being.Turkish archives of pediatrics · 2024Article
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
- Confounding Factor Analysis for Vocal Fold Oscillations.Entropy (Basel, Switzerland) · 2023Article
- A smartphone-based application for cough counting in patients with acute asthma exacerbation.Journal of thoracic disease · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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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.