ArticleScientific reports2021
Identifying individuals with recent COVID-19 through voice classification using deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Long-term COVID-19 symptoms and post-vaccination reactions among prolonged COVID-19 patients in the Kurdistan region of Iraq.Data in brief · 2025Article
- Fused Audio Instance and Representation for Respiratory Disease Detection.Sensors (Basel, Switzerland) · 2024Article
- Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers.Chinese medicine · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
- Self-perception of health and physical activity levels among the youth and adults before and amidst the COVID-19 pandemic.Frontiers in public health · 2024Article
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.Sensors (Basel, Switzerland) · 2023Article
- Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.Sensors (Basel, Switzerland) · 2023Article
- Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.Knowledge-based systems · 2022Article
- Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics.Scientific reports · 2022Article
- Voice Features of Sustained Phoneme as COVID-19 Biomarker.IEEE journal of translational engineering in health and medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an 'ah' sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic 'ah' sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.
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