Challenges and Opportunities in Deploying COVID-19 Cough AI Systems

Khanzada, Amil; Hegde, Siddhi; Sreeram, Shreya; Bower, Grace; Wang, William; Mediratta, Rishi P.; Meister, Kara D; Rameau, Anaïs

doi:10.1016/j.jvoice.2021.08.009

Cited by 11 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the adopted rigorous methodology and the homogenous population of this study (same ethnicity, language and nationality) support the quality of our results, hopefully dispelling some skepticism towards this pioneering screening technology. Wider multicultural and multilanguage study should be designed to confirm our findings among international populations, in order to rapidly answer the pressing need for a more effective surveillance strategy for COVID-19 [104].…”

Section: Discussionmentioning

confidence: 80%

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

et al. 2024

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Discussionmentioning

confidence: 80%

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

et al. 2024

View full text Add to dashboard Cite

show abstract

“…For example, a recent study on the quality of a dataset containing EHRs and COVID-19 test results for thousands of patients in Portugal ("SINAVE-Med") found over 90% of the data were missing for important features such as the date of patients' first positive laboratory test results or the indication of whether or not a positive case required intensive care unit (ICU) admission [53]. Another study on the use of wearable devices for COVID-19 research found that many patients stop wearing their devices or let the charge expire during the time when they are symptomatic [19], and, despite its prevalence in multiple large datasets, potential audio clipping in crowdsourced COVID-19 cough data can impact the reliability of timefrequency representations in discriminative neural networks and lead to poor diagnostic performance as well [54]. A summary of how the issue of missing data is relevant for various types of COVID-19 health data is presented in Table I (short version) and Suppl.…”

Section: Quality Control For Missing Datamentioning

confidence: 99%

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Isgut

Gloster

Choi

et al. 2023

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

show abstract

“…The current ubiquitous availability of audio recording hardware via cellular technology and exponential advances in data analytics via deep learning have created unprecedented opportunities for collecting and analyzing cough sounds. Several promising results have been published in the distinction of normal and abnormal cough sounds, 6 screening of disease such as COVID-19, [7][8][9] and prediction of disease severity in common respiratory conditions like asthma, 10 chronic bronchitis, 11 and croup. 12,13 Diagnosis of respiratory conditions in low-resource regions is fraught with difficulties due to the lack of field-deployable imaging and laboratory facilities, as well as the scarcity of trained community health care workers, and cough sound biomarkers could help improve screening and diagnostics in such settings.…”

Section: Introductionmentioning

confidence: 99%

Cough Sounds in Screening and Diagnostics: A Scoping Review

Hegde,

Sreeram,

Alter

et al. 2023

The Laryngoscope

Self Cite

View full text Add to dashboard Cite

ObjectiveThe aim of the study was to examine applications of cough sounds towards screening tools and diagnostics in the biomedical and engineering literature, with particular focus on disease types, acoustic data collection protocols, data processing and analytics, accuracy, and limitations.Data SourcesPubMed, EMBASE, Web of Science, Scopus, Cochrane Library, IEEE Xplore, Engineering Village, and ACM Digital Library were searched from inception to August 2021.Review MethodsA scoping review was conducted on screening and diagnostic uses of cough sounds in adults, children, and animals, in English peer‐reviewed and gray literature of any design.ResultsFrom a total of 438 abstracts screened, 108 articles met inclusion criteria. Human studies were most common (77.8%); the majority focused on adults (57.3%). Single‐modality acoustic data collection was most common (71.2%), with few multimodal studies, including plethysmography (15.7%) and clinico‐demographic data (7.4%). Data analytics methods were highly variable, with 61.1% using machine learning, the majority of which (78.8%) were published after 2010. Studies commonly focused on cough detection (41.7%) and screening of COVID‐19 (11.1%); among pediatric studies, the most common focus was diagnosis of asthma (52.6%).ConclusionThough the use of cough sounds in diagnostics is not new, academic interest has accelerated in the past decade. Cough sound offers the possibility of an accessible, noninvasive, and low‐cost disease biomarker, particularly in the era of rapid development of machine learning capabilities in combination with the ubiquity of cellular technology with high‐quality recording capability. However, most cough sound literature hinges on nonstandardized data collection protocols and small, nondiverse, single‐modality datasets, with limited external validity. Laryngoscope, 2023

show abstract

Challenges and Opportunities in Deploying COVID-19 Cough AI Systems

Cited by 11 publications

References 7 publications

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Cough Sounds in Screening and Diagnostics: A Scoping Review

Contact Info

Product

Resources

About