ArticleIEEE journal of selected topics in signal processing2022
Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough.
Article in IEEE journal of selected topics in signal processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- A large-scale and PCR-referenced vocal audio dataset for COVID-19.Scientific data · 2024Article
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review.Journal of medical Internet research · 2023Article
- Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.Sensors (Basel, Switzerland) · 2023Article
- A smartphone-based application for cough counting in patients with acute asthma exacerbation.Journal of thoracic disease · 2023Article
- Interpretation of lung disease classification with light attention connected module.Biomedical signal processing and control · 2023Article
- Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection.Scientific data · 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
- Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.Sensors (Basel, Switzerland) · 2023Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- A summary of the ComParE COVID-19 challenges.Frontiers in digital health · 2023Article
- Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.Experimental biology and medicine (Maywood, N.J.) · 2022Review
- Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.IEEE journal of biomedical and health informatics · 2022Article
- Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.Annals of medicine and surgery (2012) · 2022Review
- Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review.IEEE open journal of engineering in medicine and biology · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic created significant interest and demand for infection detection and monitoring solutions. In this paper, we propose a machine learning method to quickly detect COVID-19 using audio recordings made on consumer devices. The approach combines signal processing and noise removal methods with an ensemble of fine-tuned deep learning networks and enables COVID detection on coughs. We have also developed and deployed a mobile application that uses a symptoms checker together with voice, breath, and cough signals to detect COVID-19 infection. The application showed robust performance on both openly sourced datasets and the noisy data collected during beta testing by the end users.
Indexed as
Identifiers
What OpenQuestion holds
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.