Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respirator...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1173 |
| _version_ | 1797296990832295936 |
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| author | Panagiotis Kapetanidis Fotios Kalioras Constantinos Tsakonas Pantelis Tzamalis George Kontogiannis Theodora Karamanidou Thanos G. Stavropoulos Sotiris Nikoletseas |
| author_facet | Panagiotis Kapetanidis Fotios Kalioras Constantinos Tsakonas Pantelis Tzamalis George Kontogiannis Theodora Karamanidou Thanos G. Stavropoulos Sotiris Nikoletseas |
| author_sort | Panagiotis Kapetanidis |
| collection | DOAJ |
| description | Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems. |
| first_indexed | 2024-03-07T22:14:44Z |
| format | Article |
| id | doaj.art-3402de8e935f469eb859e8e6ac0fd40f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:14:44Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3402de8e935f469eb859e8e6ac0fd40f2024-02-23T15:33:45ZengMDPI AGSensors1424-82202024-02-01244117310.3390/s24041173Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic ReviewPanagiotis Kapetanidis0Fotios Kalioras1Constantinos Tsakonas2Pantelis Tzamalis3George Kontogiannis4Theodora Karamanidou5Thanos G. Stavropoulos6Sotiris Nikoletseas7Computer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreecePfizer Center for Digital Innovation, 55535 Thessaloniki, GreecePfizer Center for Digital Innovation, 55535 Thessaloniki, GreeceComputer Engineering and Informatics Department, University of Patras, 26504 Patras, GreeceRespiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.https://www.mdpi.com/1424-8220/24/4/1173respiratory symptomsrespiratory diseaseaudio analysissignal processingmachine learningdigital biomarkers |
| spellingShingle | Panagiotis Kapetanidis Fotios Kalioras Constantinos Tsakonas Pantelis Tzamalis George Kontogiannis Theodora Karamanidou Thanos G. Stavropoulos Sotiris Nikoletseas Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review Sensors respiratory symptoms respiratory disease audio analysis signal processing machine learning digital biomarkers |
| title | Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review |
| title_full | Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review |
| title_fullStr | Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review |
| title_full_unstemmed | Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review |
| title_short | Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review |
| title_sort | respiratory diseases diagnosis using audio analysis and artificial intelligence a systematic review |
| topic | respiratory symptoms respiratory disease audio analysis signal processing machine learning digital biomarkers |
| url | https://www.mdpi.com/1424-8220/24/4/1173 |
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