Towards sound based testing of COVID-19—Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge

Sharma, Neeraj; Muguli, Ananya; Krishnan, Prashant; Kumar, Rohit; Chetupalli, Srikanth Raj; Ganapathy, Sriram

doi:10.1016/j.csl.2021.101320

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…The deep-CNN model obtained an accuracy of 94.45% in distinguishing COVID-19 from other respiratory infections. Sharma et al (2022) proposed a binary classification method that distinguished COVID-19 and non-COVID-19 cases. The dataset contained heavy cough sounds and other voice modalities of 1040 patients.…”

Section: Covid-19 Diagnosis Using Voice-based Analysismentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

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In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

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Section: Covid-19 Diagnosis Using Voice-based Analysismentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

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“…Focusing on cough sound samples, Brown et al [29] report an AUC of 0.82. Further, in the first Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge [34], 29 teams report AUC between 0.55−0.87 on cough sound samples taken from a subset of Coswara dataset. A few studies have also explored using breathing [28], [29], [30] and sustained phonation of vowel sounds [32], [35] for COVID-19 detection.…”

Section: B Acoustics For Respiratory Diagnosticsmentioning

confidence: 99%

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms

Chetupalli

Krishnan

Sharma

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Background: The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. Objective: In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. Methods: We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. Results: We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality (p < 0.05). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. Conclusion: The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.

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“…27 Volunteers recorded and uploaded samples on a smartphone or computer, and the database divided them into COVID or non-COVID cohorts. 28 Numerous researchers have used this database to train AI models for detection of COVID-19 based on cough or breath recordings. 22,25,29 Prior work used data from pre-Omicron variants (including Alpha and Delta), which affected the voice less commonly than Omicron.…”

Section: Ai For Covid-19 Testingmentioning

confidence: 99%

“…"Coswara" is a database containing COVID-19 respiratory sound samples, including cough, breath, and scripted voice data 25 . Samples recorded and uploaded by volunteers on a smartphone or computer were divided into COVID and non-COVID cohorts 26 . Numerous researchers have used this database to train AI models for COVID-19 detection 20,23,27 .…”

Section: Social Media Data For Clinical Tasksmentioning

confidence: 99%

Omicron detection with large language models and YouTube audio data

Anibal

Landa

Nguyen

et al. 2022

Preprint

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Social media data can boost artificial intelligence (AI) systems designed for clinical applications by expanding data sources that are otherwise limited in size. Currently, deep learning methods applied to large social media datasets are used for a variety of biomedical tasks, including forecasting the onset of mental illness and detecting outbreaks of new diseases. However, exploration of online data as a training component for diagnostics tools remains rare, despite the deluge of information that is available through various APIs. In this study, data from YouTube was used to train a model to detect the Omicron variants of SARS-CoV-2 from changes in the human voice. According to the ZOE Health Study, laryngitis and hoarse voice were among the most common symptoms of the Omicron variant, regardless of vaccination status.1 Omicron is characterized by pre-symptomatic transmission as well as mild or absent symptoms. Therefore, impactful screening methodologies may benefit from speed, convenience, and non-invasive ergonomics. We mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods where the Omicron variant (or sub-variants, including BA.4/5) consisted of more than 95% of cases.2,3,4 Our dataset contained 183 distinct Omicron samples (28.39 hours), 192 healthy samples (33.90 hours), 138 samples from other upper respiratory infections (8.09 hours), and 133 samples from non-Omicron variants of COVID-19 (22.84 hours). We used a flexible data collection protocol and implemented a simple augmentation strategy that leveraged intra-sample variance arising from the diversity of unscripted speech (different words, phrases, and tones). This approach led to enhanced model generalization despite a relatively small number of samples. We trained a DenseNet model to detect Omicron in subjects with self-declared positive COVID-19 tests. Our model achieved 86% sensitivity and 81% specificity when detecting healthy voices (asymptomatic negative vs. all positive). We also achieved 76% sensitivity and 70% specificity separating between symptomatic negative samples and all positive samples. This result showed that social media data may be used to counterbalance the limited amount of well-curated data commonly available for deep learning tasks in clinical medicine. Our work demonstrates the potential of digital, non-invasive diagnostic methods trained with public online data and explores novel design paradigms for diagnostic tools that rely on audio data.

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Towards sound based testing of COVID-19—Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge

Cited by 7 publications

References 39 publications

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms

Omicron detection with large language models and YouTube audio data

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