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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, Nehad M Abdel Rahman; Alhaidari, Fahd A; Shaikh, Fatema S; Alsenbel, Yasmine M; Alalharith, Dima M; Alharthi, Hajar M; Alghamdi, Wejdan M; Alshahrani, Mohammed S.
Affiliation
  • Alqudaihi KS; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Aslam N; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Khan IU; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Almuhaideb AM; Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alsunaidi SJ; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Ibrahim NMAR; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alhaidari FA; Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Shaikh FS; Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alsenbel YM; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alalharith DM; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alharthi HM; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alghamdi WM; Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
  • Alshahrani MS; Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia.
IEEE Access ; 9: 102327-102344, 2021.
Article in En | MEDLINE | ID: mdl-34786317
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: IEEE Access Year: 2021 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: IEEE Access Year: 2021 Type: Article