{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T18:43:00Z","timestamp":1792608180862,"version":"4.5.0"},"reference-count":169,"publisher":"European Respiratory Society (ERS)","issue":"180","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir Rev"],"accepted":{"date-parts":[[2026,3,30]]},"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>Cough is a common and physiologically informative component of respiratory morbidity, but its potential for diagnosing and monitoring disease is not thoroughly investigated. This systematic review synthesised the literature on algorithmic and statistical models analysing cough acoustics for diagnosing or monitoring respiratory conditions. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, five databases (PubMed, Embase, Scopus, Web of Science and CENTRAL) were systematically searched for studies published from January 2010 to June 2025. Eligible studies performed quantitative acoustic feature analysis of human coughs using statistical, machine learning or deep learning models and reported diagnostic or prognostic performance. 89 studies from 34 countries were assessed, covering cough detection (n=31), disease classification (n=55) and disease severity prediction (n=3), reflecting potential applications in disease monitoring. Deep learning approaches, especially convolutional and recurrent networks, were predominant (n=56) and tended to achieve the higher accuracies, although machine learning ensemble methods and logistic regression also demonstrated strong performance, particularly with well-engineered features. Across different diseases, sensitivities and specificities were often reported to be \u226590%, notably for tuberculosis, asthma and COVID-19. However, methodological weaknesses were common, with only 11.2% of studies introducing external validation, 71.1\u201387.6% demonstrating high risk of bias (according to PROBAST-AI) and most based on small, homogeneous or crowdsourced cohorts with limited generalisability. These limitations contribute to inflated internal performance and uncertainty about real-world applicability. Cough acoustic biomarkers hold promise as an adjunctive tool for screening and longitudinal monitoring in low-resource environments. Nevertheless, widespread implementation will require large, multicentre validation, standardised calibration, bias control and incorporation into privacy-preserving workflows.<\/jats:p>","DOI":"10.1183\/16000617.0288-2025","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:30:44Z","timestamp":1782289844000},"page":"250288","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review"],"prefix":"10.1183","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1908-537X","authenticated-orcid":false,"given":"Jinming","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thayabaran","family":"Kathiresan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alveena","family":"Siddiqui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5270-1333","authenticated-orcid":false,"given":"Stuart B.","family":"Mazzone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3505-2631","authenticated-orcid":false,"given":"Adam","family":"Vogel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"2026062401301454000_35.180.250288.1","doi-asserted-by":"crossref","first-page":"101936","DOI":"10.1016\/j.eclinm.2023.101936","article-title":"Global burden of chronic respiratory diseases and risk factors, 1990\u20132019: an update from the Global Burden of Disease Study 2019","volume":"59","author":"Momtazmanesh","year":"2023","journal-title":"eClinicalMedicine"},{"key":"2026062401301454000_35.180.250288.2","unstructured":"World Health Organization . The top 10 causes of death . Date last accessed: 12 February 2026. Date last updated: 7 August 2024. www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death"},{"key":"2026062401301454000_35.180.250288.3","doi-asserted-by":"crossref","first-page":"116448","DOI":"10.1016\/j.envres.2023.116448","article-title":"Climate change and mortality rates of COPD and asthma: a global analysis from 2000 to 2018","volume":"233","author":"Tran","year":"2023","journal-title":"Environ Res"},{"key":"2026062401301454000_35.180.250288.4","unstructured":"Forum of International Respiratory Societies . The Global Impact of Respiratory Disease. 3rd Edn. Sheffield, European Respiratory Society, 2021."},{"key":"2026062401301454000_35.180.250288.5","doi-asserted-by":"crossref","first-page":"e218","DOI":"10.1016\/S2214-109X(22)00506-X","article-title":"Asthma management and control in children, adolescents, and adults in 25 countries: a Global Asthma Network phase I cross-sectional study","volume":"11","author":"Garc\u00eda-Marcos","year":"2023","journal-title":"Lancet Glob Health"},{"key":"2026062401301454000_35.180.250288.6","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.3390\/app13042378","article-title":"COVID-19 detection model with acoustic features from cough sound and its application","volume":"13","author":"Kim","year":"2023","journal-title":"Appl Sci"},{"key":"2026062401301454000_35.180.250288.7","doi-asserted-by":"crossref","first-page":"103939","DOI":"10.1016\/j.ebiom.2022.103939","article-title":"Reimagining the status quo: how close are we to rapid sputum-free tuberculosis diagnostics for all?","volume":"78","author":"Nathavitharana","year":"2022","journal-title":"eBioMedicine"},{"key":"2026062401301454000_35.180.250288.8","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2021.10.030"},{"key":"2026062401301454000_35.180.250288.9","doi-asserted-by":"crossref","first-page":"101034","DOI":"10.1016\/j.eclinm.2021.101034","article-title":"How to improve access to medical imaging in low- and middle-income countries?","volume":"38","author":"Frija","year":"2021","journal-title":"eClinicalMedicine"},{"key":"2026062401301454000_35.180.250288.10","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.3390\/s22082896","article-title":"Past and trends in cough sound acquisition, automatic detection and automatic classification: a comparative review","volume":"22","author":"Serrurier","year":"2022","journal-title":"Sensors (Basel)"},{"key":"2026062401301454000_35.180.250288.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.08.2086"},{"key":"2026062401301454000_35.180.250288.12","doi-asserted-by":"publisher","DOI":"10.1183\/23120541.00053-2022"},{"key":"2026062401301454000_35.180.250288.13","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1002\/lary.31042","article-title":"Cough sounds in screening and diagnostics: a scoping review","volume":"134","author":"Hegde","year":"2024","journal-title":"Laryngoscope"},{"key":"2026062401301454000_35.180.250288.14","doi-asserted-by":"crossref","first-page":"2004271","DOI":"10.1183\/13993003.04271-2020","article-title":"Performance of a digital signal processing algorithm for the accurate quantification of cough frequency","volume":"58","author":"Smith","year":"2021","journal-title":"Eur Respir"},{"key":"2026062401301454000_35.180.250288.15","doi-asserted-by":"crossref","first-page":"971.e9","DOI":"10.1016\/j.jvoice.2021.05.015","article-title":"Analysis of COVID-19 resulting cough using formants and automatic speech recognition system","volume":"37","author":"Zealouk","year":"2023","journal-title":"J Voice"},{"key":"2026062401301454000_35.180.250288.16","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1007\/s00455-023-10574-1","article-title":"Acoustic analysis of voluntary coughs, throat clearings, and induced reflexive coughs in a healthy population","volume":"38","author":"Mootassim-Billah","year":"2023","journal-title":"Dysphagia"},{"key":"2026062401301454000_35.180.250288.17","doi-asserted-by":"crossref","unstructured":"Adhi Pramono RX , Anas Imtiaz S , Rodriguez-Villegas E . Automatic cough detection in acoustic signal using spectral features. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin , Germany , 2019; pp. 7153\u20137156. doi:10.1109\/EMBC.2019.8857792","DOI":"10.1109\/EMBC.2019.8857792"},{"key":"2026062401301454000_35.180.250288.18","doi-asserted-by":"crossref","unstructured":"Miranda IDS , Diacon AH , Niesler TR . A comparative study of features for acoustic cough detection using deep architectures. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin , Germany , 2019; pp. 2601\u20132605. doi:10.1109\/EMBC.2019.8856412","DOI":"10.1109\/EMBC.2019.8856412"},{"key":"2026062401301454000_35.180.250288.19","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1038\/s41598-023-50371-2","article-title":"Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments","volume":"14","author":"Ghrabli","year":"2024","journal-title":"Sci Rep"},{"key":"2026062401301454000_35.180.250288.20","doi-asserted-by":"crossref","first-page":"104283","DOI":"10.1016\/j.jbi.2023.104283","article-title":"Original digital cough monitoring \u2013 a potential predictive acoustic biomarker of clinical outcomes in hospitalized COVID-19 patients","volume":"138","author":"Altshuler","year":"2023","journal-title":"J Biomed Inform"},{"key":"2026062401301454000_35.180.250288.21","doi-asserted-by":"publisher","DOI":"10.1007\/s00408-017-9996-2"},{"key":"2026062401301454000_35.180.250288.22","doi-asserted-by":"publisher","DOI":"10.1186\/1745-9974-6-4"},{"key":"2026062401301454000_35.180.250288.23","doi-asserted-by":"crossref","first-page":"e958","DOI":"10.7717\/peerj-cs.958","article-title":"A systematic review on cough sound analysis for COVID-19 diagnosis and screening: is my cough sound COVID-19?","volume":"8","author":"Santosh","year":"2022","journal-title":"PeerJ Comput Sci"},{"key":"2026062401301454000_35.180.250288.24","doi-asserted-by":"crossref","first-page":"e44804","DOI":"10.2196\/44804","article-title":"Evaluating listening performance for COVID-19 detection by clinicians and machine learning: comparative study","volume":"25","author":"Han","year":"2023","journal-title":"J Med Internet Res"},{"key":"2026062401301454000_35.180.250288.25","doi-asserted-by":"crossref","first-page":"7036","DOI":"10.3390\/s21217036","article-title":"Diagnosis of pneumonia by cough sounds analyzed with statistical features and AI","volume":"21","author":"Chung","year":"2021","journal-title":"Sensors (Basel)"},{"key":"2026062401301454000_35.180.250288.26","doi-asserted-by":"crossref","unstructured":"Lavorini F , Bernacchi G , Fabbrizzi A . Clinical methods for the study of cough. In: Zanasi A , Fontana GA , Mutolo D , eds. Cough: Pathophysiology Diagnosis and Treatment. Cham, Springer International Publishing, 2020; pp. 127\u2013136. doi:10.1007\/978-3-030-48571-9_9","DOI":"10.1007\/978-3-030-48571-9_9"},{"key":"2026062401301454000_35.180.250288.27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1745-9974-2-1","article-title":"The description of cough sounds by healthcare professionals","volume":"2","author":"Smith","year":"2006","journal-title":"Cough"},{"key":"2026062401301454000_35.180.250288.28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1000097"},{"key":"2026062401301454000_35.180.250288.29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003583"},{"key":"2026062401301454000_35.180.250288.30","doi-asserted-by":"publisher","DOI":"10.5195\/jmla.2018.345"},{"key":"2026062401301454000_35.180.250288.31","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505"},{"key":"2026062401301454000_35.180.250288.32","doi-asserted-by":"crossref","unstructured":"Adhi Pramono RX , Anas Imtiaz S , Rodriguez-Villegas E . Automatic identification of cough events from acoustic signals. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin , Germany , 2019; pp. 217\u2013220. doi:10.1109\/EMBC.2019.8856420","DOI":"10.1109\/EMBC.2019.8856420"},{"key":"2026062401301454000_35.180.250288.33","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.bspc.2015.05.001","article-title":"Automatic cough segmentation from non-contact sound recordings in pediatric wards","volume":"21","author":"Amrulloh","year":"2015","journal-title":"Biomed Signal Process Control"},{"key":"2026062401301454000_35.180.250288.34","doi-asserted-by":"crossref","first-page":"e38439","DOI":"10.2196\/38439","article-title":"Nighttime continuous contactless smartphone-based cough monitoring for the ward: validation study","volume":"7","author":"Barata","year":"2023","journal-title":"JMIR Form Res"},{"key":"2026062401301454000_35.180.250288.35","doi-asserted-by":"crossref","unstructured":"Barata F , Kipfer K , Weber M , et al. Towards device-agnostic mobile cough detection with convolutional neural networks. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), Xi'an , China , 2019; pp. 1\u201311. doi:10.1109\/ICHI.2019.8904554","DOI":"10.1109\/ICHI.2019.8904554"},{"key":"2026062401301454000_35.180.250288.36","doi-asserted-by":"crossref","first-page":"e18082","DOI":"10.2196\/18082","article-title":"Automatic recognition, segmentation, and sex assignment of nocturnal asthmatic coughs and cough epochs in smartphone audio recordings: observational field study","volume":"22","author":"Barata","year":"2020","journal-title":"J Med Internet Res"},{"key":"2026062401301454000_35.180.250288.37","doi-asserted-by":"crossref","first-page":"3210","DOI":"10.1109\/JBHI.2023.3264783","article-title":"Robust cough detection with out-of-distribution detection","volume":"27","author":"Chen","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026062401301454000_35.180.250288.38","doi-asserted-by":"publisher","DOI":"10.1183\/23120541.00265-2022"},{"key":"2026062401301454000_35.180.250288.39","doi-asserted-by":"crossref","first-page":"118816","DOI":"10.1109\/ACCESS.2024.3449370","article-title":"Neural cough counter: a novel deep learning approach for cough detection and monitoring","volume":"12","author":"Feng","year":"2024","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.40","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1177\/00202940221101667","article-title":"Cough classification of MEMS microphone signal using time-series and tabular machine learning algorithms","volume":"56","author":"Han","year":"2023","journal-title":"Meas Control"},{"key":"2026062401301454000_35.180.250288.41","doi-asserted-by":"crossref","unstructured":"Sharan RV , Xiong H , Berkovsky S . Detecting cough recordings in crowdsourced data using CNN-RNN. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece , 2022; pp. 1\u20134. doi:10.1109\/BHI56158.2022.9926896","DOI":"10.1109\/BHI56158.2022.9926896"},{"key":"2026062401301454000_35.180.250288.42","doi-asserted-by":"crossref","unstructured":"den Brinker AC , Coman M , Ouweltjes O , et al. Performance requirements for cough classifiers in real-world applications. In: 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam , Netherlands , 2021; pp. 96\u2013100. doi:10.23919\/Eusipco47968.2020.9287860","DOI":"10.23919\/Eusipco47968.2020.9287860"},{"key":"2026062401301454000_35.180.250288.43","doi-asserted-by":"crossref","unstructured":"Kadambi P , Mohanty A , Ren H , et al. Towards a wearable cough detector based on neural networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada , 2018; pp. 2161\u20132165. doi:10.1109\/ICASSP.2018.8461394","DOI":"10.1109\/ICASSP.2018.8461394"},{"key":"2026062401301454000_35.180.250288.44","doi-asserted-by":"publisher","DOI":"10.1016\/j.resp.2018.03.010"},{"key":"2026062401301454000_35.180.250288.45","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1002\/ppul.25801","article-title":"Development and technical validation of a smartphone-based pediatric cough detection algorithm","volume":"57","author":"Kruizinga","year":"2022","journal-title":"Pediatr Pulmonol"},{"key":"2026062401301454000_35.180.250288.46","doi-asserted-by":"publisher","DOI":"10.1183\/23120541.00279-2022"},{"key":"2026062401301454000_35.180.250288.47","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1472-6947-15-S4-S2","article-title":"Cough event classification by pretrained deep neural network","volume":"15","author":"Liu","year":"2015","journal-title":"BMC Med Inform Decis Mak"},{"key":"2026062401301454000_35.180.250288.48","doi-asserted-by":"crossref","unstructured":"M\u0142y\u0144czak M , Pariaszewska K , Cybulski G . Automatic cough episode detection using a vibroacoustic sensor. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy , 2015; pp. 2808\u20132811. doi:10.1109\/EMBC.2015.7318975","DOI":"10.1109\/EMBC.2015.7318975"},{"key":"2026062401301454000_35.180.250288.49","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/JBHI.2018.2800741","article-title":"Robust detection of audio-cough events using local Hu moments","volume":"23","author":"Monge-Alvarez","year":"2019","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026062401301454000_35.180.250288.50","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.bspc.2023.104580","article-title":"Cough sound detection from raw waveform using SincNet and bidirectional GRU","volume":"82","author":"Sharan","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"2026062401301454000_35.180.250288.51","doi-asserted-by":"publisher","DOI":"10.21037\/jtd-22-1492"},{"key":"2026062401301454000_35.180.250288.52","doi-asserted-by":"crossref","first-page":"951621","DOI":"10.1155\/2014\/951621","article-title":"Automated cough assessment on a mobile platform","volume":"2014","author":"Sterling","year":"2014","journal-title":"J Med Eng"},{"key":"2026062401301454000_35.180.250288.53","doi-asserted-by":"crossref","unstructured":"Swarnkar V , Abeyratne UR , Amrulloh Y , et al. Neural network based algorithm for automatic identification of cough sounds. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan , 2013. pp. 1764\u20131767. doi:10.1109\/EMBC.2013.6609862","DOI":"10.1109\/EMBC.2013.6609862"},{"key":"2026062401301454000_35.180.250288.54","doi-asserted-by":"crossref","unstructured":"Teyhouee A , Osgood ND . Cough detection using hidden Markov models. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation , Washington, USA , 2019; pp. 266\u2013276. doi:10.1007\/978-3-030-21741-9_27","DOI":"10.1007\/978-3-030-21741-9_27"},{"key":"2026062401301454000_35.180.250288.55","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2014.10.003"},{"key":"2026062401301454000_35.180.250288.56","doi-asserted-by":"crossref","first-page":"5941","DOI":"10.1109\/JBHI.2024.3415479","article-title":"Development of a miniaturized mechanoacoustic sensor for continuous, objective cough detection, characterization and physiologic monitoring in children with cystic fibrosis","volume":"28","author":"Tzavelis","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026062401301454000_35.180.250288.57","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/OJEMB.2023.3271457","article-title":"Environment knowledge-driven generic models to detect coughs from audio recordings","volume":"4","author":"Vhaduri","year":"2023","journal-title":"IEEE Open J Eng Med Biol"},{"key":"2026062401301454000_35.180.250288.58","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.3390\/bioengineering11111105","article-title":"Automated cough analysis with convolutional recurrent neural network","volume":"11","author":"Wang","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"2026062401301454000_35.180.250288.59","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.bspc.2016.11.005","article-title":"Cough detection by ensembling multiple frequency subband features","volume":"33","author":"You","year":"2017","journal-title":"Biomed Signal Process Control"},{"key":"2026062401301454000_35.180.250288.60","doi-asserted-by":"crossref","first-page":"103304","DOI":"10.1016\/j.bspc.2021.103304","article-title":"Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression","volume":"72","author":"You","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"2026062401301454000_35.180.250288.61","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/978-3-031-50078-7_23","article-title":"CASCO: a contactless cough screening system based on audio signal processing","volume":"14498","author":"Zhang","year":"2024","journal-title":"Adv Comput Graph"},{"key":"2026062401301454000_35.180.250288.62","doi-asserted-by":"publisher","DOI":"10.1109\/89.326616"},{"key":"2026062401301454000_35.180.250288.63","unstructured":"Ellis DPW . PLP and RASTA (and MFCC, and inversion) in Matlab using melfcc.m and invmelfcc.m. Date last accessed: 12 February 2026. Date last updated: 3 September 2012. https:\/\/www.ee.columbia.edu\/\u223cdpwe\/LabROSA\/matlab\/rastamat\/"},{"key":"2026062401301454000_35.180.250288.64","doi-asserted-by":"publisher","DOI":"10.3109\/03091902.2012.758322"},{"key":"2026062401301454000_35.180.250288.65","doi-asserted-by":"crossref","first-page":"16857","DOI":"10.1007\/s00521-024-09895-5","article-title":"Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds","volume":"36","author":"Barua","year":"2024","journal-title":"Neural Comput Appl"},{"key":"2026062401301454000_35.180.250288.66","doi-asserted-by":"crossref","first-page":"107184","DOI":"10.1016\/j.engappai.2023.107184","article-title":"GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds","volume":"127","author":"Kilic","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"2026062401301454000_35.180.250288.67","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.3390\/diagnostics15091155","article-title":"Automated lightweight model for asthma detection using respiratory and cough sound signals","volume":"15","author":"Xu","year":"2025","journal-title":"Diagnostics (Basel)"},{"key":"2026062401301454000_35.180.250288.68","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s10489-024-06028-2","article-title":"Self-supervised learning for intelligent disease diagnosis using audio signals: beyond COPD to a spectrum of diseases","volume":"55","author":"Sun","year":"2025","journal-title":"Appl Intell"},{"key":"2026062401301454000_35.180.250288.69","doi-asserted-by":"crossref","unstructured":"Brown C , Chauhan J , Grammenos A , et al. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In: KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, 2020; pp. 3474\u20133484. doi:10.1145\/3394486.3412865","DOI":"10.1145\/3394486.3412865"},{"key":"2026062401301454000_35.180.250288.70","doi-asserted-by":"crossref","first-page":"92554","DOI":"10.1109\/ACCESS.2025.3572202","article-title":"Toward AI-driven cough sound analysis for respiratory disease diagnosis","volume":"13","author":"Benaliouche","year":"2025","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.71","doi-asserted-by":"crossref","unstructured":"Hossain MZ , Uddin MdB , Yang Y , et al. CovidEnvelope: an automated fast approach to diagnose COVID-19 from cough signals. In: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane , Australia , 2021; pp. 1\u20136. doi:10.1109\/CSDE53843.2021.9718501","DOI":"10.1109\/CSDE53843.2021.9718501"},{"key":"2026062401301454000_35.180.250288.72","doi-asserted-by":"crossref","unstructured":"Pal A , Sankarasubbu M . Pay attention to the cough: early diagnosis of COVID-19 using interpretable symptoms embeddings with cough sound signal processing. In: SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing, Virtual Event, 2021; pp. 620\u2013628. doi:10.1145\/3412841.3441943","DOI":"10.1145\/3412841.3441943"},{"key":"2026062401301454000_35.180.250288.73","doi-asserted-by":"publisher","DOI":"10.1136\/bmjinnov-2021-000668"},{"key":"2026062401301454000_35.180.250288.74","doi-asserted-by":"crossref","unstructured":"Feng K , He F , Steinmann J , et al. Deep-learning based approach to identify Covid-19. In: SoutheastCon 2021, Atlanta , USA , 2021; pp. 1\u20134. doi:10.1109\/SoutheastCon45413.2021.9401826","DOI":"10.1109\/SoutheastCon45413.2021.9401826"},{"key":"2026062401301454000_35.180.250288.75","doi-asserted-by":"publisher","DOI":"10.1016\/j.bea.2022.100025"},{"key":"2026062401301454000_35.180.250288.76","doi-asserted-by":"publisher","DOI":"10.2196\/44410"},{"key":"2026062401301454000_35.180.250288.77","first-page":"100129","article-title":"Malaysian cough sound analysis and COVID-19 classification with deep learning","volume":"9","author":"Kho","year":"2024","journal-title":"Intell-Based Med"},{"key":"2026062401301454000_35.180.250288.78","first-page":"95","article-title":"Diagnosis of COVID-19 infection via association rules of cough encoding","volume":"17","author":"Mohammed","year":"2022","journal-title":"ECTI Trans Comput Inf Technol"},{"key":"2026062401301454000_35.180.250288.79","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.imed.2023.01.001","article-title":"An evolutionary ensemble learning for diagnosing COVID-19 via cough signals","volume":"3","author":"Najaran","year":"2023","journal-title":"Intell Med"},{"key":"2026062401301454000_35.180.250288.80","doi-asserted-by":"crossref","first-page":"105153","DOI":"10.1016\/j.compbiomed.2021.105153","article-title":"COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features","volume":"141","author":"Pahar","year":"2022","journal-title":"Comput Biol Med"},{"key":"2026062401301454000_35.180.250288.81","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3390\/bioengineering11010055","article-title":"An AI-enabled bias-free respiratory disease diagnosis model using cough audio","volume":"11","author":"Saeed","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"2026062401301454000_35.180.250288.82","doi-asserted-by":"crossref","unstructured":"Shati A , Hassan GM , Datta A . COVID-19 detection system: a comparative analysis of system performance based on acoustic features of cough audio signals. In: 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Exeter , UK , 2023; pp. 2706\u20132713. doi:10.1109\/TrustCom60117.2023.00377","DOI":"10.1109\/TrustCom60117.2023.00377"},{"key":"2026062401301454000_35.180.250288.83","doi-asserted-by":"crossref","first-page":"986293","DOI":"10.3389\/frsip.2022.986293","article-title":"COVID-19 respiratory sound analysis and classification using audio textures","volume":"2","author":"Silva","year":"2022","journal-title":"Front Signal Process"},{"key":"2026062401301454000_35.180.250288.84","doi-asserted-by":"crossref","first-page":"5","DOI":"10.7546\/ijba.2025.29.1.000931","article-title":"Classification of COVID-19 using temporal and spectral features of cough sounds","volume":"29","author":"Abera Tessema","year":"2025","journal-title":"Int J Bioautomation"},{"key":"2026062401301454000_35.180.250288.85","doi-asserted-by":"crossref","first-page":"e0162128","DOI":"10.1371\/journal.pone.0162128","article-title":"A cough-based algorithm for automatic diagnosis of pertussis","volume":"11","author":"Pramono","year":"2016","journal-title":"PLoS One"},{"key":"2026062401301454000_35.180.250288.86","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1007\/s10439-013-0836-0","article-title":"Cough sound analysis can rapidly diagnose childhood pneumonia","volume":"41","author":"Abeyratne","year":"2013","journal-title":"Ann Biomed Eng"},{"key":"2026062401301454000_35.180.250288.87","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2381214"},{"key":"2026062401301454000_35.180.250288.88","doi-asserted-by":"crossref","unstructured":"Sharan RV , Qian K , Yamamoto Y . Detecting childhood pneumonia using handcrafted and deep learning cough sound features and multilayer perceptron. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney , Australia , 2023; pp. 1\u20134. doi:10.1109\/EMBC40787.2023.10340477","DOI":"10.1109\/EMBC40787.2023.10340477"},{"key":"2026062401301454000_35.180.250288.89","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/JBHI.2023.3327292","article-title":"Automated cough sound analysis for detecting childhood pneumonia","volume":"28","author":"Sharan","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026062401301454000_35.180.250288.90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ACCESS.2024.3519296","article-title":"A novel cascaded approach for classification of tuberculosis using cough audio in real-time environment","volume":"12","author":"Mahmood","year":"2024","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.91","doi-asserted-by":"crossref","first-page":"10.1088\/1361-6579\/ac2fb8","DOI":"10.1088\/1361-6579\/ac2fb8","article-title":"Automatic cough classification for tuberculosis screening in a real-world environment","volume":"42","author":"Pahar","year":"2021","journal-title":"Physiol Meas"},{"key":"2026062401301454000_35.180.250288.92","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s44163-024-00179-4","article-title":"Detection of tuberculosis using cough audio analysis: a deep learning approach with capsule networks","volume":"4","author":"Rajasekar","year":"2024","journal-title":"Discov Artif Intell"},{"key":"2026062401301454000_35.180.250288.93","doi-asserted-by":"crossref","first-page":"e0302651","DOI":"10.1371\/journal.pone.0302651","article-title":"Feature fusion method for pulmonary tuberculosis patient detection based on cough sound","volume":"19","author":"Xu","year":"2024","journal-title":"PLoS One"},{"key":"2026062401301454000_35.180.250288.94","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-31772-9"},{"key":"2026062401301454000_35.180.250288.95","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.3390\/electronics13071177","article-title":"Advancing cough classification: Swin transformer vs. 2D CNN with STFT and augmentation techniques","volume":"13","author":"Ghourabi","year":"2024","journal-title":"Electronics"},{"key":"2026062401301454000_35.180.250288.96","doi-asserted-by":"crossref","unstructured":"Infante C , Chamberlain DB , Kodgule R , et al. Classification of voluntary coughs applied to the screening of respiratory disease. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea , 2017; pp. 1413\u20131416. doi:10.1109\/EMBC.2017.8037098","DOI":"10.1109\/EMBC.2017.8037098"},{"key":"2026062401301454000_35.180.250288.97","doi-asserted-by":"crossref","first-page":"133958","DOI":"10.1109\/ACCESS.2022.3231640","article-title":"Productive and non-productive cough classification using biologically inspired techniques","volume":"10","author":"Sharan","year":"2022","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.98","doi-asserted-by":"crossref","unstructured":"Swarnkar V , Abeyratne UR , Amrulloh YA , et al. Automated algorithm for wet\/dry cough sounds classification. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego , USA , 2012; pp. 3147\u20133150. doi:10.1109\/EMBC.2012.6346632","DOI":"10.1109\/EMBC.2012.6346632"},{"key":"2026062401301454000_35.180.250288.99","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-013-0741-6"},{"key":"2026062401301454000_35.180.250288.100","doi-asserted-by":"crossref","first-page":"28241","DOI":"10.1007\/s10489-023-05006-4","article-title":"Federated few-shot learning for cough classification with edge devices","volume":"53","author":"Hoang","year":"2023","journal-title":"Appl Intell"},{"key":"2026062401301454000_35.180.250288.101","doi-asserted-by":"crossref","first-page":"103870","DOI":"10.1016\/j.medengphy.2022.103870","article-title":"DKPNet41: directed knight pattern network-based cough sound classification model for automatic disease diagnosis","volume":"110","author":"Kuluozturk","year":"2022","journal-title":"Med Eng Phys"},{"key":"2026062401301454000_35.180.250288.102","doi-asserted-by":"crossref","unstructured":"Infante C , Chamberlain D , Fletcher R , et al. Use of cough sounds for diagnosis and screening of pulmonary disease. In: 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, USA , 2017; pp. 1\u201310. doi:10.1109\/GHTC.2017.8239338","DOI":"10.1109\/GHTC.2017.8239338"},{"key":"2026062401301454000_35.180.250288.103","doi-asserted-by":"crossref","unstructured":"Ceylan M , Rassouli F , Boesch M , et al. Cough classification of unknown emerging respiratory disease with federated learning. In: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando , USA , 2024; pp. 1\u20135. doi:10.1109\/EMBC53108.2024.10781930","DOI":"10.1109\/EMBC53108.2024.10781930"},{"key":"2026062401301454000_35.180.250288.104","doi-asserted-by":"crossref","first-page":"100319","DOI":"10.1016\/j.imu.2020.100319","article-title":"Cough sound analysis and objective correlation with spirometry and clinical diagnosis","volume":"19","author":"Rudraraju","year":"2020","journal-title":"Inform Med Unlocked"},{"key":"2026062401301454000_35.180.250288.105","doi-asserted-by":"crossref","unstructured":"Sreeram ASK , Ravishankar U , Sripada NR , et al. Investigating the potential of MFCC features in classifying respiratory diseases. In: 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Paris , France , 2020; pp. 1\u20137. doi:10.1109\/IOTSMS52051.2020.9340166","DOI":"10.1109\/IOTSMS52051.2020.9340166"},{"key":"2026062401301454000_35.180.250288.106","doi-asserted-by":"publisher","DOI":"10.1186\/s12931-019-1046-6"},{"key":"2026062401301454000_35.180.250288.107","doi-asserted-by":"crossref","first-page":"77507","DOI":"10.1109\/ACCESS.2025.3565969","article-title":"Acute respiratory infections identification with cough sounds and overlapping patch modulated vision transformers","volume":"13","author":"Kishore","year":"2025","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.108","doi-asserted-by":"crossref","first-page":"5555","DOI":"10.3390\/s21165555","article-title":"Deep neural network-based respiratory pathology classification using cough sounds","volume":"21","author":"Balamurali","year":"2021","journal-title":"Sensors (Basel)"},{"key":"2026062401301454000_35.180.250288.109","doi-asserted-by":"publisher","DOI":"10.1145\/3448107"},{"key":"2026062401301454000_35.180.250288.110","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10916-025-02154-7","article-title":"Edge computing system for automatic detection of chronic respiratory diseases using audio analysis","volume":"49","author":"Rivas-Navarrete","year":"2025","journal-title":"J Med Syst"},{"key":"2026062401301454000_35.180.250288.111","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/JBHI.2018.2872038","article-title":"TussisWatch: a smart-phone system to identify cough episodes as early symptoms of chronic obstructive pulmonary disease and congestive heart failure","volume":"23","author":"Windmon","year":"2019","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026062401301454000_35.180.250288.112","doi-asserted-by":"crossref","first-page":"103703","DOI":"10.1016\/j.bspc.2022.103703","article-title":"Audio texture analysis of COVID-19 cough, breath, and speech sounds","volume":"76","author":"Sharma","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"2026062401301454000_35.180.250288.113","doi-asserted-by":"crossref","first-page":"108843","DOI":"10.1016\/j.compbiomed.2024.108843","article-title":"Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification","volume":"179","author":"Shen","year":"2024","journal-title":"Comput Biol Med"},{"key":"2026062401301454000_35.180.250288.114","doi-asserted-by":"crossref","first-page":"100378","DOI":"10.1016\/j.imu.2020.100378","article-title":"AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app","volume":"20","author":"Imran","year":"2020","journal-title":"Inform Med Unlocked"},{"key":"2026062401301454000_35.180.250288.115","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.1109\/ACCESS.2023.3347410","article-title":"DMRNet based tuberculosis screening with cough sound","volume":"12","author":"Xu","year":"2024","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.116","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1080\/02770903.2019.1684516","article-title":"Stratifying asthma severity in children using cough sound analytic technology","volume":"58","author":"Swarnkar","year":"2021","journal-title":"J Asthma"},{"key":"2026062401301454000_35.180.250288.117","doi-asserted-by":"crossref","first-page":"1015876","DOI":"10.3389\/fpubh.2022.1015876","article-title":"A forced cough sound based pulmonary function assessment method by using machine learning","volume":"10","author":"Xu","year":"2022","journal-title":"Front Public Health"},{"key":"2026062401301454000_35.180.250288.118","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0272-5231(21)01014-5","article-title":"Pathophysiology of cough","volume":"8","author":"McCool","year":"1987","journal-title":"Clin Chest Med"},{"key":"2026062401301454000_35.180.250288.119","doi-asserted-by":"crossref","first-page":"100618","DOI":"10.1016\/j.waojou.2021.100618","article-title":"WAO-ARIA consensus on chronic cough - part II: phenotypes and mechanisms of abnormal cough presentation \u2014 updates in COVID-19","volume":"14","author":"Rouadi","year":"2021","journal-title":"World Allergy Organ J"},{"key":"2026062401301454000_35.180.250288.120","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1007\/978-3-031-56703-2_22","article-title":"Efficient characterization of cough sounds using statistical analysis","volume":"2054","author":"Naveenkumar","year":"2024","journal-title":"Adv Comput"},{"key":"2026062401301454000_35.180.250288.121","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-024-03590-x"},{"key":"2026062401301454000_35.180.250288.122","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s10462-024-11009-5","article-title":"Digital phenotypes and digital biomarkers for health and diseases: a systematic review of machine learning approaches utilizing passive non-invasive signals collected via wearable devices and smartphones","volume":"58","author":"Sameh","year":"2024","journal-title":"Artif Intell Rev"},{"key":"2026062401301454000_35.180.250288.123","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s12890-022-01896-1","article-title":"The coming era of a new auscultation system for analyzing respiratory sounds","volume":"22","author":"Kim","year":"2022","journal-title":"BMC Pulm Med"},{"key":"2026062401301454000_35.180.250288.124","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1186\/s12890-025-03846-z","article-title":"Cough duration, energy and sound frequency in COVID-19 patients: the spectral analysis results","volume":"25","author":"Budnevsky","year":"2025","journal-title":"BMC Pulm Med"},{"key":"2026062401301454000_35.180.250288.125","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/j.jvoice.2022.06.011","article-title":"The acoustic dissection of cough: diving into machine listening-based COVID-19 analysis and detection","volume":"38","author":"Ren","year":"2024","journal-title":"J Voice"},{"key":"2026062401301454000_35.180.250288.126","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/RBME.2018.2874353","article-title":"Acoustic methods for pulmonary diagnosis","volume":"12","author":"Rao","year":"2019","journal-title":"IEEE Rev Biomed Eng"},{"key":"2026062401301454000_35.180.250288.127","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1164\/ajrccm.161.6.9812057","article-title":"Alterations in airway wall properties in infants with a history of wheezing disorders","volume":"161","author":"Frey","year":"2000","journal-title":"Am J Respir Crit Care Med"},{"key":"2026062401301454000_35.180.250288.128","doi-asserted-by":"publisher","DOI":"10.1007\/s00408-024-00734-x"},{"key":"2026062401301454000_35.180.250288.129","doi-asserted-by":"crossref","first-page":"114318","DOI":"10.1109\/ACCESS.2025.3584401","article-title":"Using cough sounds for the recognition of multiclass respiratory diseases with artificial intelligence: a review","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"key":"2026062401301454000_35.180.250288.130","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1186\/s12911-025-03065-w","article-title":"Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish","volume":"25","author":"Han","year":"2025","journal-title":"BMC Med Inform Decis Mak"},{"key":"2026062401301454000_35.180.250288.131","doi-asserted-by":"crossref","unstructured":"Steyerberg EW . Statistical models for prediction. In: Steyerberg EW , ed. Clinical Prediction Models. Cham, Springer International Publishing, 2019; pp. 59\u201393. doi:10.1007\/978-3-030-16399-0_4","DOI":"10.1007\/978-3-030-16399-0_4"},{"key":"2026062401301454000_35.180.250288.132","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.873548"},{"key":"2026062401301454000_35.180.250288.133","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1606181"},{"key":"2026062401301454000_35.180.250288.134","doi-asserted-by":"publisher","DOI":"10.3390\/medicina56090455"},{"key":"2026062401301454000_35.180.250288.135","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"2026062401301454000_35.180.250288.136","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"2026062401301454000_35.180.250288.137","doi-asserted-by":"crossref","first-page":"e220232","DOI":"10.1148\/ryai.220232","article-title":"A guide to cross-validation for artificial intelligence in medical imaging","volume":"5","author":"Bradshaw","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"2026062401301454000_35.180.250288.138","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"Sn Comput Sci"},{"key":"2026062401301454000_35.180.250288.139","doi-asserted-by":"crossref","first-page":"104765","DOI":"10.1016\/j.compbiomed.2021.104765","article-title":"COVID-19 detection with traditional and deep features on cough acoustic signals","volume":"136","author":"Erdo\u011fan","year":"2021","journal-title":"Comput Biol Med"},{"key":"2026062401301454000_35.180.250288.140","doi-asserted-by":"crossref","first-page":"103257","DOI":"10.1016\/j.eclinm.2025.103257","article-title":"Diagnostic accuracy of point-of-care triage tests for pulmonary tuberculosis using host blood protein biomarkers: a systematic review and meta-analysis","volume":"84","author":"Komakech","year":"2025","journal-title":"eClinicalMedicine"},{"key":"2026062401301454000_35.180.250288.141","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41597-021-00937-4","article-title":"The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms","volume":"8","author":"Orlandic","year":"2021","journal-title":"Sci Data"},{"key":"2026062401301454000_35.180.250288.142","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1038\/s41597-023-02266-0","article-title":"Coswara: a respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection","volume":"10","author":"Bhattacharya","year":"2023","journal-title":"Sci Data"},{"key":"2026062401301454000_35.180.250288.143","doi-asserted-by":"crossref","first-page":"21990","DOI":"10.1038\/s41598-022-26492-5","article-title":"Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening","volume":"12","author":"Xiong","year":"2022","journal-title":"Sci Rep"},{"key":"2026062401301454000_35.180.250288.144","doi-asserted-by":"crossref","first-page":"108439","DOI":"10.1016\/j.engappai.2024.108439","article-title":"Label noise correction for crowdsourcing using dynamic resampling","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"2026062401301454000_35.180.250288.145","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2014.09.018"},{"key":"2026062401301454000_35.180.250288.146","doi-asserted-by":"publisher","DOI":"10.1378\/chest.14-1481"},{"key":"2026062401301454000_35.180.250288.147","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adi0282"},{"key":"2026062401301454000_35.180.250288.148","doi-asserted-by":"crossref","first-page":"1477694","DOI":"10.3389\/fbioe.2024.1477694","article-title":"An automatic cough counting method and system construction for portable devices","volume":"12","author":"Wang","year":"2024","journal-title":"Front Bioeng Biotechnol"},{"key":"2026062401301454000_35.180.250288.149","doi-asserted-by":"crossref","first-page":"108844","DOI":"10.1016\/j.compbiomed.2024.108844","article-title":"Exploring explainable AI features in the vocal biomarkers of lung disease","volume":"179","author":"Chen","year":"2024","journal-title":"Comput Biol Med"},{"key":"2026062401301454000_35.180.250288.150","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/OJEMB.2020.3026928","article-title":"COVID-19 artificial intelligence diagnosis using only cough recordings","volume":"1","author":"Laguarta","year":"2020","journal-title":"IEEE Open J Eng Med Biol"},{"key":"2026062401301454000_35.180.250288.151","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1038\/s42256-023-00773-8","article-title":"Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers","volume":"6","author":"Coppock","year":"2024","journal-title":"Nat Mach Intell"},{"key":"2026062401301454000_35.180.250288.152","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/s41746-021-00553-x","article-title":"Sounds of COVID-19: exploring realistic performance of audio-based digital testing","volume":"5","author":"Han","year":"2022","journal-title":"NPJ Digit Med"},{"key":"2026062401301454000_35.180.250288.153","doi-asserted-by":"publisher","DOI":"10.3399\/BJGP.2020.0750"},{"key":"2026062401301454000_35.180.250288.154","doi-asserted-by":"crossref","first-page":"107911","DOI":"10.1016\/j.cmpb.2023.107911","article-title":"A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases","volume":"243","author":"Kumar","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"2026062401301454000_35.180.250288.155","doi-asserted-by":"crossref","unstructured":"Moghimi MJ , Bradley C , Maire N , et al. Multimodal wearable platform for remote monitoring of breathing patterns, cough events and blood oxygen level. In: Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables III, San Francisco, USA , 2022; pp. 100\u2013104. doi:10.1117\/12.2607680","DOI":"10.1117\/12.2607680"},{"key":"2026062401301454000_35.180.250288.156","unstructured":"Fuhlbrigge AL , Choi AMK . Diagnostic procedures in respiratory disease. In: Jameson JL , Fauci AS , Kasper DL , et al. , eds. Harrisons Principles of Internal Medicine. 20th Edn. New York, McGraw-Hill Education, 2018."},{"key":"2026062401301454000_35.180.250288.157","doi-asserted-by":"crossref","first-page":"8056","DOI":"10.3390\/s21238056","article-title":"Cough sounds recorded via smart devices as useful non-invasive digital biomarkers of aspiration risk: a case report","volume":"21","author":"Kang","year":"2021","journal-title":"Sensors (Basel)"},{"key":"2026062401301454000_35.180.250288.158","doi-asserted-by":"crossref","first-page":"100974","DOI":"10.37349\/eaa.2025.100974","article-title":"Telemedicine and remote monitoring in asthma care: innovations triggered by the pandemic","volume":"3","author":"Ledda","year":"2025","journal-title":"Explor Asthma Allergy"},{"key":"2026062401301454000_35.180.250288.159","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.2147\/PPA.S399635","article-title":"Recall bias in the assessment of cough for patients discharged from lung surgery","volume":"17","author":"Su","year":"2023","journal-title":"Patient Prefer Adherence"},{"key":"2026062401301454000_35.180.250288.160","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s00408-021-00435-9","article-title":"Domiciliary cough monitoring for the prediction of COPD exacerbations","volume":"199","author":"Crooks","year":"2021","journal-title":"Lung"},{"key":"2026062401301454000_35.180.250288.161","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1038\/s41598-025-85341-3","article-title":"Validation and accuracy of the Hyfe cough monitoring system: a multicenter clinical study","volume":"15","author":"Chaccour","year":"2025","journal-title":"Sci Rep"},{"key":"2026062401301454000_35.180.250288.162","first-page":"A3187","article-title":"Stepwise validation program for developing the algorithm for chronic cough with remote e-stethoscope platform RESP","volume":"205","author":"Glass","year":"2022","journal-title":"Am J Respir Crit Care Med"},{"key":"2026062401301454000_35.180.250288.163","first-page":"A4536","article-title":"Audio-based automated cough analysis system: a proof of concept study","volume":"209","author":"Taylor","year":"2024","journal-title":"Am J Respir Crit Care Med"},{"key":"2026062401301454000_35.180.250288.164","doi-asserted-by":"publisher","DOI":"10.1136\/bmjinnov-2021-000673"},{"key":"2026062401301454000_35.180.250288.165","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.3390\/app15073629","article-title":"Internet of things-based healthcare systems: an overview of privacy-preserving mechanisms","volume":"15","author":"Nabha","year":"2025","journal-title":"Appl Sci"},{"key":"2026062401301454000_35.180.250288.166","doi-asserted-by":"publisher","DOI":"10.1145\/3643540"},{"key":"2026062401301454000_35.180.250288.167","doi-asserted-by":"crossref","unstructured":"Larson EC , Lee T , Liu S , et al. Accurate and privacy preserving cough sensing using a low-cost microphone. In: UbiComp '11: Proceedings of the 13th International Conference on Ubiquitous Computing, New York , USA , 2011; pp. 375\u2013384. doi:10.1145\/2030112.2030163","DOI":"10.1145\/2030112.2030163"},{"key":"2026062401301454000_35.180.250288.168","doi-asserted-by":"crossref","unstructured":"Ponsiglione AM , Angelone F , Sparaco R , et al. Enabling COVID-19 detection from multiple audio recordings: a preliminary comparison between cough, breath, and speech signals. In: 9th European Medical and Biological Engineering Conference. EMBEC 2024, Cham, Switzerland , 2024; pp. 373\u2013383. doi:10.1007\/978-3-031-61625-9_40","DOI":"10.1007\/978-3-031-61625-9_40"},{"key":"2026062401301454000_35.180.250288.169","doi-asserted-by":"crossref","first-page":"419","DOI":"10.2307\/2982993","article-title":"Publication bias: a problem in interpreting medical data","volume":"151","author":"Begg","year":"1988","journal-title":"J R Stat Soc Ser A Stat Soc"}],"container-title":["European Respiratory Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1183\/16000617.0288-2025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T08:31:49Z","timestamp":1782289909000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.ersnet.org\/lookup\/doi\/10.1183\/16000617.0288-2025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":169,"journal-issue":{"issue":"180","published-online":{"date-parts":[[2026,6,24]]},"published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1183\/16000617.0288-2025"],"URL":"https:\/\/doi.org\/10.1183\/16000617.0288-2025","relation":{},"ISSN":["0905-9180","1600-0617"],"issn-type":[{"value":"0905-9180","type":"print"},{"value":"1600-0617","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]}}}