Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey
Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healt...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482100294X |
| _version_ | 1818157670961512448 |
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| author | Aneeqa Ijaz Muhammad Nabeel Usama Masood Tahir Mahmood Mydah Sajid Hashmi Iryna Posokhova Ali Rizwan Ali Imran |
| author_facet | Aneeqa Ijaz Muhammad Nabeel Usama Masood Tahir Mahmood Mydah Sajid Hashmi Iryna Posokhova Ali Rizwan Ali Imran |
| author_sort | Aneeqa Ijaz |
| collection | DOAJ |
| description | Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI- powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail. |
| first_indexed | 2024-12-11T15:17:53Z |
| format | Article |
| id | doaj.art-f826c8ac29404110a970cc70434eb8be |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-11T15:17:53Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-f826c8ac29404110a970cc70434eb8be2022-12-22T01:00:30ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0129100832Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A surveyAneeqa Ijaz0Muhammad Nabeel1Usama Masood2Tahir Mahmood3Mydah Sajid Hashmi4Iryna Posokhova5Ali Rizwan6Ali Imran7AI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USA; Corresponding author.AI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USAAI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USAAI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USAUniversity of Pittsburgh Medical Center, USAKharkiv National Medical University, UkraineDepartment of Electrical Engineering, Qatar University, QatarAI4Networks Research Center, Dept. of Electrical & Computer Engineering, University of Oklahoma, USACough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI- powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.http://www.sciencedirect.com/science/article/pii/S235291482100294XRespiratory conditionsCoughClassificationDiagnosisArtificial intelligenceMachine learning |
| spellingShingle | Aneeqa Ijaz Muhammad Nabeel Usama Masood Tahir Mahmood Mydah Sajid Hashmi Iryna Posokhova Ali Rizwan Ali Imran Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey Informatics in Medicine Unlocked Respiratory conditions Cough Classification Diagnosis Artificial intelligence Machine learning |
| title | Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey |
| title_full | Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey |
| title_fullStr | Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey |
| title_full_unstemmed | Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey |
| title_short | Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey |
| title_sort | towards using cough for respiratory disease diagnosis by leveraging artificial intelligence a survey |
| topic | Respiratory conditions Cough Classification Diagnosis Artificial intelligence Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S235291482100294X |
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