Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Alqudaihi, Kawther S.; Aslam, Nida; Khan, Irfan Ullah; Almuhaideb, Abdullah M.; Alsunaidi, Shikah J.; Ibrahim, N. S.; Alhaidari, Fahd; Shaikh, Fatema; Alsenbel, Yasmine M.; Alalharith, Dima M.; Alharthi, Hajar M.; Alghamdi, Wejdan M.; Alshahrani, Mohammed

doi:10.1109/access.2021.3097559

Cited by 68 publications

(33 citation statements)

References 91 publications

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“…In the case of Apps and sponsored projects, all of them were at the audio collection stage [ [36][37][38][39][40][41][42], [43]]. On-board real-time AI processing had not yet been implemented in most systems.…”

Section: Search Results Of Covid-19 Apps Based On Cough Breathing And...mentioning

confidence: 99%

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review

Husain

Simpkin

Gibbons

et al. 2022

IEEE Open J. Eng. Med. Biol.

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Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1 st April 2020 and 15 st August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-underthe-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 for cough-, breathing-or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. However, the proposed methods with some time and appropriate clinical testing would prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in human body.

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Section: Search Results Of Covid-19 Apps Based On Cough Breathing And...mentioning

confidence: 99%

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review

Husain

Simpkin

Gibbons

et al. 2022

IEEE Open J. Eng. Med. Biol.

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“…Malfunction associated with COVID-19 cases can be discovered from these patterns of the patient breath. Recently, the research of COVID-19 diagnosis algorithms based on cough and breath signals has got more attention (Bagad et al 2020 ; Rasheed et al 2020 ; Alqudaihi et al 2021 ; Pal and Sankarasubbu 2021 ). Therefore, using ML and DL algorithms to detect and screen COVID-19 cases from cough and breath samples has revealed encouraging outcomes (Belkacem et al 2021 ; Verde et al 2021 ; Pahar et al 2021 ; Deshpande et al 2022 ).…”

Section: Preliminariesmentioning

confidence: 99%

CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications

Hemdan

El‐Shafai

Sayed

2022

J Ambient Intell Human Comput

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Today, there is a level of panic and chaos dominating the entire world due to the massive outbreak in the second wave of COVID-19 disease. As the disease has numerous symptoms ranging from a simple fever to the inability to breathe, which may lead to death. One of these symptoms is a cough which is considered one of the most common symptoms for COVID-19 disease. Recent research shows that the cough of a COVID-19 patient has distinct features that are different from other diseases. Consequently, the cough sound can be detected and classified to be used as a preliminary diagnosis of the COVID-19, which will help in reducing the spreading of that disease. The artificial intelligence (AI) engine can diagnose COVID-19 diseases by executing differential analysis of its inherent characteristics and comparing it to other non-COVID-19 coughs. However, the diagnosis of a COVID-19 infection by cough alone is an extremely challenging multidisciplinary problem. Therefore, this paper proposes a hybrid framework for efficiently COVID-19 detection and diagnosis using various ML algorithms from cough audio signals. The accuracy of this framework is improved with the utilization of the genetic algorithm with the ML techniques. We also assess the proposed system called CR19 for diagnosis on metrics such as precision, recall, F-measure. The results proved that the hybrid (GA-ML) technique provides superior results based on different evaluation metrics compared with ML approaches such as LR, LDA, KNN, CART, NB, and SVM. The proposed framework achieve an accuracy equal to 92.19%, 94.32%, 97.87%, 92.19%, 91.48%, and 93.61% in compared with the ML are 90.78, 92.90, 95.74, 87.94, 81.56, and 92.198 for LR, LDA, KNN, CART, NB, and SVM respectively. The proposed framework will efficiently help the physicians provide a proper medical decision regarding the COVID-19 analysis, thereby saving more lives. Therefore, this CR19 framework can be a clinical decision assistance tool used to channel clinical testing and treatment to those who need it the most, thereby saving more lives.

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“…Besides these technical developments, researchers are emphasizing the need for research in COVID19-induced brain dysfunction (CIBD) to improve the mental health of large populations of infected/uninfected individuals [288]. Despite these highlighted areas and developments, AI's role in COVID-19 and post COVID-19 era can offer a proven method to further strengthen the impact of healthcare services/applications on population health, which is more necessary than ever in the post-COVID era [289][290][291][292]. To this end, further developments are needed to lower the effects of this pandemic and to serve mankind effectively in these unanticipated and challenging times.…”

Section: Future Research Directionsmentioning

confidence: 99%

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Majeed

Hwang

2021

Symmetry

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This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

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Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Cited by 68 publications

References 91 publications

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review

CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

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