Observational studyJournal of medical Internet research2024
Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study.
Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- The Effects of COVID-19 on Voice.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- National Trends in the Prevalence of Unmet Health Care and Dental Care Needs During the COVID-19 Pandemic: Longitudinal Study in South Korea, 2009-2022.JMIR public health and surveillance · 2024Article
- Artificial Intelligence Challenges in the Healthcare Industry: A Systematic Review of Recent Evidence.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe outbreak of SARS-CoV-2 in 2019 has necessitated the rapid and accurate detection of COVID-19 to manage patients effectively and implement public health measures. Artificial intelligence (AI) models analyzing cough sounds have emerged as promising tools for large-scale screening and early identification of potential cases.
objectiveThis study aimed to investigate the efficacy of using cough sounds as a diagnostic tool for COVID-19, considering the unique acoustic features that differentiate positive and negative cases. We investigated whether an AI model trained on cough sound recordings from specific periods, especially the early stages of the COVID-19 pandemic, were applicable to the ongoing situation with persistent variants.
methodsWe used cough sound recordings from 3 data sets (Cambridge, Coswara, and Virufy) representing different stages of the pandemic and variants. Our AI model was trained using the Cambridge data set with subsequent evaluation against all data sets. The performance was analyzed based on the area under the receiver operating curve (AUC) across different data measurement periods and COVID-19 variants.
resultsThe AI model demonstrated a high AUC when tested with the Cambridge data set, indicative of its initial effectiveness. However, the performance varied significantly with other data sets, particularly in detecting later variants such as Delta and Omicron, with a marked decline in AUC observed for the latter. These results highlight the challenges in maintaining the efficacy of AI models against the backdrop of an evolving virus.
conclusionsWhile AI models analyzing cough sounds offer a promising noninvasive and rapid screening method for COVID-19, their effectiveness is challenged by the emergence of new virus variants. Ongoing research and adaptations in AI methodologies are crucial to address these limitations. The adaptability of AI models to evolve with the virus underscores their potential as a foundational technology for not only the current pandemic but also future outbreaks, contributing to a more agile and resilient global health infrastructure.
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