Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough

Chaudhari, Gunvant; Jiang, Xinyi; Fakhry, Ahmed; Han, Asriel; Xiao, Jaclyn; Shen, Sabrina; Khanzada, Amil

doi:10.48550/arxiv.2011.13320

Cited by 28 publications

(57 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, not all the labels from the COUGHVID dataset have been verified. Second, due to the difference between data collection methods and intention to maintain anonymity, COUGHVID dataset does not have some of the metadata that may be documented in other datasets [6], for example, race information. This situation might propose some difficulty as we expand our model to more datasets.…”

Section: Onclusion and Discussionmentioning

confidence: 99%

“…After the success of the multi-branch model, which we have proposed in our previous work [6] with a maximum value of 0.80 AUC for COUGHVID and Coswara datasets combined, we now propose the state of the art deep learning model for COUGHVID dataset with micro-average AUC of 0.91. As illustrated in table 4.1, the sensitivity and specificity of detecting COVID-19 are 85% and 99.2%, respectively, which shows the high diagnostic performance of our network.…”

Section: R Esultsmentioning

confidence: 99%

“…The most commonly collected audio recordings for detecting COVID-19 are coughs. Some groups, such as Coswara [19] and Virufy [6], collect additional counting and vowel data along with cough recordings. However, this collected data varies substantially in sound quality and formats, depending on the environment and instrument used.…”

Section: Introductionmentioning

confidence: 99%

“…The state-of-the-art performance of AI algorithms for detecting COVID-19 ranges from 0.68 AUC to 0.97 AUC [2,15], based on several factors, such as the quality of audio files used to train the learning algorithm, preprocessing methodology, and the algorithm structure. In our previous work [6], our group developed a deep learning model with an average AUC of 0.77 across several datasets, including Coswara [19], COUGHVID [16], and Virufy [6] datasets. In this paper, we build on our previous approach and present the state-of-the-art deep learning model on the COUGHVID dataset.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multi-Branch Deep Learning Network for Automated Detection of COVID-19

Fakhry

Jiang²,

Xiao³

et al. 2021

Interspeech 2021

Self Cite

View full text Add to dashboard Cite

Fast and affordable solutions for COVID-19 testing are necessary to contain the spread of the global pandemic and help relieve the burden on medical facilities. Currently, limited testing locations and expensive equipment pose difficulties for individuals trying to be tested, especially in low-resource settings. Researchers have successfully presented models for detecting COVID-19 infection status using audio samples recorded in clinical settings [5, 15], suggesting that audio-based Artificial Intelligence models can be used to identify COVID-19. Such models have the potential to be deployed on smartphones for fast, widespread, and low-resource testing. However, while previous studies have trained models on cleaned audio samples collected mainly from clinical settings, audio samples collected from average smartphones may yield suboptimal quality data that is different from the clean data that models were trained on. This discrepancy may add a bias that affects COVID-19 status predictions. To tackle this issue, we propose a multi-branch deep learning network that is trained and tested on crowdsourced data where most of the data has not been manually processed and cleaned. Furthermore, the model achieves state-of-art results for the COUGHVID dataset [16]. After breaking down results for each category, we have shown an AUC of 0.99 for audio samples with COVID-19 positive labels.

show abstract

Section: Onclusion and Discussionmentioning

confidence: 99%

Section: R Esultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-Branch Deep Learning Network for Automated Detection of COVID-19

Fakhry

Jiang²,

Xiao³

et al. 2021

Interspeech 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chaudhari Gunavant et al [25] their research show that crowdsourced cough audio samples collected worldwide on smartphones; various groups have gathered several COVID-19 cough recording datasets and used them to train machine learning models for COVID-19 detection. However, each of these models has been trained on data from a variety of formats and recording settings; collected additional counting and vocal recordings, authors exclusively collect cough recordings.…”

Section: Literature Review and Background Workmentioning

confidence: 99%

A literature review on COVID-19 disease diagnosis from respiratory sound data

Kumar¹,

Pja

2021

AIMS Bioengineering

View full text Add to dashboard Cite

The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day; in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.

show abstract