{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:16:16Z","timestamp":1793502976905,"version":"4.5.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Arch Otorhinolaryngol"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00405-025-09808-9","type":"journal-article","created":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T20:48:58Z","timestamp":1763326138000},"page":"2107-2116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multichannel machine learning for polysomnographic diagnosis of obstructive sleep apnea: a Bayesian meta-analysis"],"prefix":"10.1007","volume":"283","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1619-5917","authenticated-orcid":false,"given":"Shahana","family":"Rani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2311-7845","authenticated-orcid":false,"given":"Esther Yanxin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1131-6591","authenticated-orcid":false,"given":"Joel Zuo Er","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2597-9142","authenticated-orcid":false,"given":"Nicole Kye Wen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4203-0972","authenticated-orcid":false,"given":"Adele Chin Wei","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1390-6445","authenticated-orcid":false,"given":"Zhou Hao","family":"Leong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4434-8258","authenticated-orcid":false,"given":"Chu Qin","family":"Phua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9753-2000","authenticated-orcid":false,"given":"Thun How","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3384-4854","authenticated-orcid":false,"given":"Leong Chai","family":"Leow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2480-4965","authenticated-orcid":false,"given":"Guang-Bin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9411-6164","authenticated-orcid":false,"given":"Benjamin Kye Jyn","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2077-2457","authenticated-orcid":false,"given":"Song Tar","family":"Toh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"9808_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-023-01496-3","volume":"8","author":"R Lv","year":"2023","unstructured":"Lv R, Liu X, Zhang Y, Chen X, Wang Y, Li Y (2023) Pathophysiological mechanisms and therapeutic approaches in obstructive sleep apnea syndrome. Signal Transduct Target Ther 8:218","journal-title":"Signal Transduct Target Ther"},{"issue":"8","key":"9808_CR2","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","volume":"7","author":"AV Benjafield","year":"2019","unstructured":"Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ et al (2019) Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med 7(8):687\u2013698","journal-title":"Lancet Respir Med"},{"issue":"3","key":"9808_CR3","first-page":"257","volume":"14","author":"A Faria","year":"2021","unstructured":"Faria A, Allen AH, Fox N, Ayas N, Laher I (2021) The public health burden of obstructive sleep apnea. Sleep Sci 14(3):257\u2013265","journal-title":"Sleep Sci"},{"key":"9808_CR4","doi-asserted-by":"crossref","first-page":"e56","DOI":"10.1161\/CIR.0000000000000988","volume":"144","author":"Y Yeghiazarians","year":"2021","unstructured":"Yeghiazarians Y, Jneid H, Tietjens JR, Redline S, Brown DL, El-Sherif N, Mehra R, Bozkurt B, Ndumele CE, Somers VK, on behalf of the American Heart Association Council on Clinical Cardiology; Council on Peripheral Vascular Disease; Council on Arteriosclerosis (2021) Thrombosis and vascular Biology; Council on Cardiopulmonary, critical Care, perioperative and Resuscitation; stroke Council; and Council on cardiovascular surgery and Anesthesia. Obstructive sleep apnea and cardiovascular disease: a scientific statement from the American heart association. Circulation 144:e56\u2013e67","journal-title":"Circulation"},{"issue":"6","key":"9808_CR5","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1378\/chest.11-2214","volume":"141","author":"C Lal","year":"2012","unstructured":"Lal C, Strange C, Bachman D (2012) Neurocognitive impairment in obstructive sleep apnea. Chest 141(6):1601\u20131610","journal-title":"Chest"},{"issue":"5","key":"9808_CR6","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.5664\/jcsm.9772","volume":"18","author":"BKJ Tan","year":"2022","unstructured":"Tan BKJ, Teo YH, Tan NKW, Yap DWT, Sundar R, Lee CH et al (2022) Association of obstructive sleep apnea and nocturnal hypoxemia with all-cancer incidence and mortality: a systematic review and meta-analysis. J Clin Sleep Med 18(5):1427\u20131440","journal-title":"J Clin Sleep Med"},{"issue":"6","key":"9808_CR7","doi-asserted-by":"publisher","first-page":"2789","DOI":"10.21037\/jgo-22-153","volume":"13","author":"YH Teo","year":"2022","unstructured":"Teo YH, Tan BKJ, Tan NKW, Yap DWT, Chai YX, Teo YN et al (2022) Obstructive sleep apnea and the incidence and mortality of gastrointestinal cancers: a systematic review and meta-analysis of 5,120,837 participants. J Gastrointest Oncol 13(6):2789\u20132798","journal-title":"J Gastrointest Oncol"},{"issue":"3","key":"9808_CR8","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1513\/AnnalsATS.202108-960OC","volume":"19","author":"AJY Cheong","year":"2022","unstructured":"Cheong AJY, Tan BKJ, Teo YH, Tan NKW, Yap DWT, Sia CH et al (2022) Obstructive sleep apnea and lung cancer: a systematic review and meta-analysis. Ann Am Thorac Soc 19(3):469\u2013475","journal-title":"Ann Am Thorac Soc"},{"issue":"11","key":"9808_CR9","doi-asserted-by":"publisher","first-page":"5407","DOI":"10.1007\/s00405-022-07457-w","volume":"279","author":"BKJ Tan","year":"2022","unstructured":"Tan BKJ, Tan NKW, Teo YH, Yap DWT, Raghupathy J, Gao EY et al (2022) Association of obstructive sleep apnea with thyroid cancer incidence: a systematic review and meta-analysis. Eur Arch Otorhinolaryngol 279(11):5407\u20135414","journal-title":"Eur Arch Otorhinolaryngol"},{"key":"9808_CR10","doi-asserted-by":"crossref","unstructured":"Yap DWT, Tan NKW, Tan BKJ, Teo YH, Tan VKM, See A et al (2022) The association of obstructive sleep apnea with breast cancer incidence and mortality: a systematic review and meta-analysis. J Breast Cancer 25(3):149","DOI":"10.4048\/jbc.2022.25.e11"},{"issue":"1","key":"9808_CR11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1080\/15402002.2018.1545649","volume":"18","author":"S Garbarino","year":"2020","unstructured":"Garbarino S, Bardwell WA, Guglielmi O, Chiorri C, Bonanni E, Magnavita N (2020) Association of anxiety and depression in obstructive sleep apnea patients: a systematic review and meta-analysis. Behav Sleep Med 18(1):35\u201357","journal-title":"Behav Sleep Med"},{"issue":"1","key":"9808_CR12","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1378\/chest.07-0040","volume":"132","author":"SP Patil","year":"2007","unstructured":"Patil SP, Schneider H, Schwartz AR, Smith PL (2007) Adult obstr sleep apnea. Chest 132(1):325\u2013337","journal-title":"Chest"},{"issue":"03","key":"9808_CR13","doi-asserted-by":"publisher","first-page":"479","DOI":"10.5664\/jcsm.6506","volume":"13","author":"VK Kapur","year":"2017","unstructured":"Kapur VK, Auckley DH, Chowdhuri S, Kuhlmann DC, Mehra R, Ramar K et al (2017) Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American academy of sleep medicine clinical practice guideline. J Clin Sleep Med 13(03):479\u2013504","journal-title":"J Clin Sleep Med"},{"issue":"34","key":"9808_CR14","doi-asserted-by":"publisher","first-page":"8106","DOI":"10.12998\/wjcc.v11.i34.8106","volume":"11","author":"RK Verma","year":"2023","unstructured":"Verma RK, Dhillon G, Grewal H, Prasad V, Munjal RS, Sharma P et al (2023) Artificial intelligence in sleep medicine: present and future. World J Clin Cases 11(34):8106\u20138110","journal-title":"World J Clin Cases"},{"issue":"4","key":"9808_CR15","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s40675-020-00197-5","volume":"6","author":"LC Markun","year":"2020","unstructured":"Markun LC, Sampat A (2020) Clinician-focused overview and developments in polysomnography. Curr Sleep Med Rep 6(4):309\u2013321","journal-title":"Curr Sleep Med Rep"},{"issue":"7","key":"9808_CR16","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.5664\/jcsm.10532","volume":"19","author":"G Bazoukis","year":"2023","unstructured":"Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL et al (2023) Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 19(7):1337\u20131363","journal-title":"J Clin Sleep Med"},{"key":"9808_CR17","doi-asserted-by":"publisher","DOI":"10.4103\/singaporemedj.SMJ-2022-170","author":"ZH Leong","year":"2023","unstructured":"Leong ZH, Loh SRH, Leow LC, Ong TH, Toh ST (2023) A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data. Singapore Med J. https:\/\/doi.org\/10.4103\/singaporemedj.SMJ-2022-170","journal-title":"Singapore Med J"},{"key":"9808_CR18","doi-asserted-by":"publisher","unstructured":"Hao Y, Tan NKW, Gao EY et al (2025) Electrocardiogram heart rate variability for machine learning diagnosis of obstructive sleep apnoea: a bayesian meta-analysis. Sleep Breath 29:303. https:\/\/doi.org\/10.1007\/s11325-025-03476-z","DOI":"10.1007\/s11325-025-03476-z"},{"key":"9808_CR19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.compbiomed.2018.06.028","volume":"100","author":"SH Choi","year":"2018","unstructured":"Choi SH, Yoon H, Kim HS, Kim HB, Kwon HB, Oh SM et al (2018) Real-time apnea-hypopnea event detection during sleep by convolutional neural networks. Comput Biol Med 100:123\u2013131","journal-title":"Comput Biol Med"},{"issue":"8","key":"9808_CR20","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1109\/JBHI.2021.3064694","volume":"25","author":"S Nikkonen","year":"2021","unstructured":"Nikkonen S, Korkalainen H, Leino A, Myllymaa S, Duce B, Leppanen T et al (2021) Automatic respiratory event scoring in obstructive sleep apnea using a long short-term memory neural network. IEEE J Biomed Health Inform 25(8):2917\u20132927","journal-title":"IEEE J Biomed Health Inform"},{"key":"9808_CR21","doi-asserted-by":"crossref","unstructured":"Chauhan NK, Singh K (2018) A review on conventional machine learning vs deep learning. International Conference on Computing, Power and Communication Technologies (GUCON); 2018. pp. 347\u201352","DOI":"10.1109\/GUCON.2018.8675097"},{"issue":"15","key":"9808_CR22","doi-asserted-by":"publisher","DOI":"10.1001\/jama.283.15.2008","volume":"283","author":"DF Stroup","year":"2000","unstructured":"Stroup DF (2000) Meta-analysis of observational studies in epidemiology. A proposal for reporting. JAMA 283(15):2008","journal-title":"JAMA"},{"key":"9808_CR23","doi-asserted-by":"crossref","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. ;n71","DOI":"10.1136\/bmj.n71"},{"issue":"1","key":"9808_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-020-0897-3","volume":"20","author":"H Harrison","year":"2020","unstructured":"Harrison H, Griffin SJ, Kuhn I, Usher-Smith JA (2020) Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation. BMC Med Res Methodol 20(1):7","journal-title":"BMC Med Res Methodol"},{"issue":"8","key":"9808_CR25","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529\u2013536","journal-title":"Ann Intern Med"},{"issue":"1","key":"9808_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-023-01910-y","volume":"23","author":"E Cerullo","year":"2023","unstructured":"Cerullo E, Sutton AJ, Jones HE, Wu O, Quinn TJ, Cooper NJ (2023) Metabayesdta: codeless bayesian meta-analysis of test accuracy, with or without a gold standard. BMC Med Res Methodol 23(1):127","journal-title":"BMC Med Res Methodol"},{"issue":"6","key":"9808_CR27","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1002\/jrsm.1666","volume":"14","author":"S Mizutani","year":"2023","unstructured":"Mizutani S, Zhou Y, Tian Y, Takagi T, Ohkubo T, Hattori S (2023) DTAmetasa\u202f: an R Shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias. Res Synth Methods 14(6):916\u2013925","journal-title":"Res Synth Methods"},{"issue":"1","key":"9808_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-019-0724-x","volume":"19","author":"SC Freeman","year":"2019","unstructured":"Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ (2019) Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Med Res Methodol 19(1):81","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"9808_CR29","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1002\/jrsm.1439","volume":"12","author":"A Patel","year":"2021","unstructured":"Patel A, Cooper N, Freeman S, Sutton A (2021) Graphical enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data. Res Synth Methods 12(1):34\u201344","journal-title":"Res Synth Methods"},{"issue":"6","key":"9808_CR30","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1002\/sim.9643","volume":"42","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Huang A, Hattori S (2023) A likelihood-based sensitivity analysis for publication bias on the summary receiver operating characteristic in meta\u2010analysis of diagnostic test accuracy. Stat Med 42(6):781\u2013798","journal-title":"Stat Med"},{"key":"9808_CR31","doi-asserted-by":"crossref","unstructured":"Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M et al (2017) Stan: A Probabilistic Programming Language. J Stat Softw 76(1). Available from: http:\/\/www.jstatsoft.org\/v76\/i01\/","DOI":"10.18637\/jss.v076.i01"},{"key":"9808_CR32","unstructured":"R Core Team R: A Language and Environment for Statistical Computing. Available from: https:\/\/www.R-project.org\/"},{"key":"9808_CR33","doi-asserted-by":"publisher","first-page":"110916","DOI":"10.1109\/ACCESS.2022.3215632","volume":"10","author":"MM Moussa","year":"2022","unstructured":"Moussa MM, Alzaabi Y, Khandoker AH (2022) Explainable computer-aided detection of obstructive sleep apnea and depression. IEEE Access 10:110916\u2013110933","journal-title":"IEEE Access"},{"issue":"4","key":"9808_CR34","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s11325-021-02302-6","volume":"25","author":"Z Li","year":"2021","unstructured":"Li Z, Li Y, Zhao G, Zhang X, Xu W, Han D (2021) A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index. Sleep Breath 25(4):2065\u20132072","journal-title":"Sleep Breath"},{"key":"9808_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02493-y","author":"CC Lee","year":"2020","unstructured":"Lee CC, Wang CP, Chiang HS, Liu JW, Chen HC (2020) Applying composite physiological characteristics to assess the severity of obstructive sleep apnea. J Ambient Intell Hum Comput. https:\/\/doi.org\/10.1007\/s12652-020-02493-y","journal-title":"J Ambient Intell Hum Comput"},{"key":"9808_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102808","volume":"150","author":"D Peng","year":"2024","unstructured":"Peng D, Yue H, Tan W, Lei W, Chen G, Shi W et al (2024) A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea\/hypopnea from nasal airflow and oximetry signals. Artif Intell Med 150:102808","journal-title":"Artif Intell Med"},{"issue":"3","key":"9808_CR37","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10916-008-9129-9","volume":"32","author":"K Polat","year":"2008","unstructured":"Polat K, Yosunkaya \u015e, G\u00fcne\u015f S (2008) Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome. J Med Syst 32(3):243\u2013250","journal-title":"J Med Syst"},{"issue":"2","key":"9808_CR38","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1016\/j.eswa.2007.11.051","volume":"36","author":"K Polat","year":"2009","unstructured":"Polat K, G\u00fcne\u015f S (2009) A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst Appl 36(2):1587\u20131592","journal-title":"Expert Syst Appl"},{"key":"9808_CR39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.sleep.2024.01.015","volume":"114","author":"S Yook","year":"2024","unstructured":"Yook S, Kim D, Gupte C, Joo EY, Kim H (2024) Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity. Sleep Med 114:211\u2013219","journal-title":"Sleep Med"},{"issue":"7499","key":"9808_CR40","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1136\/bmj.330.7499.1080","volume":"330","author":"CJ Gill","year":"2005","unstructured":"Gill CJ, Sabin L, Schmid CH (2005) Why clinicians are natural bayesians. BMJ 330(7499):1080\u20131083","journal-title":"BMJ"},{"key":"9808_CR41","doi-asserted-by":"publisher","DOI":"10.2196\/58187","volume":"26","author":"A Abd-Alrazaq","year":"2024","unstructured":"Abd-Alrazaq A, Aslam H, AlSaad R, Alsahli M, Ahmed A, Damseh R, Aziz S, Sheikh J (2024) Detection of sleep apnea using wearable AI: systematic review and meta-analysis. J Med Internet Res 26:e58187","journal-title":"J Med Internet Res"},{"key":"9808_CR42","doi-asserted-by":"crossref","unstructured":"Giorgi L, Nardelli D, Moffa A, Iafrati F, Di Giovanni S, Olszewska E, Baptista P, Sabatino L, Casale M (2025) Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review. InHealthcare 13(2):181 MDPI","DOI":"10.3390\/healthcare13020181"},{"key":"9808_CR43","first-page":"1","volume":"12","author":"M Kara","year":"2025","unstructured":"Kara M, Lakner Z, Tam\u00e1s L, Moln\u00e1r V (2025) Artificial intelligence in the diagnosis of obstructive sleep apnea: a scoping review. European archives of Oto-Rhino-Laryngology 12:1\u20132","journal-title":"European archives of Oto-Rhino-Laryngology"},{"issue":"1","key":"9808_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11325-024-03173-3","volume":"29","author":"EY Gao","year":"2025","unstructured":"Gao EY, Tan BK, Tan NK, Ng AC, Leong ZH, Phua CQ, Loh SR, Uataya M, Goh LC, Ong TH, Leow LC (2025) Artificial intelligence facial recognition of obstructive sleep apnea: a bayesian meta-analysis. Sleep Breath 29(1):1\u20131","journal-title":"Sleep Breath"},{"key":"9808_CR45","doi-asserted-by":"crossref","unstructured":"Tan BK, Gao EY, Tan NK, Yeo BS, Tan CJ, Ng AC, Leong ZH, Phua CQ, Uataya M, Goh LC, Ong TH (2025) Machine listening for obstructive sleep apnea diagnosis: A bayesian Meta-Analysis. Chest. Apr 11","DOI":"10.1016\/j.chest.2025.04.006"},{"key":"9808_CR46","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241291707","volume":"10","author":"MJ Park","year":"2024","unstructured":"Park MJ, Choi JH, Kim SY, Ha TK (2024) A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography. Digit Health 10:20552076241291707","journal-title":"Digit Health"},{"issue":"1","key":"9808_CR47","doi-asserted-by":"publisher","DOI":"10.5812\/ans-144058","volume":"11","author":"B Samadi","year":"2024","unstructured":"Samadi B, Samadi S, Samadi M, Samadi S, Samadi M, Mohammadi M (2024) Systematic review of detecting sleep apnea using artificial intelligence: an insight to convolutional neural network method. Arch Neurosci 11(1):e144058","journal-title":"Arch Neurosci"},{"issue":"6","key":"9808_CR48","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1478","volume":"12","author":"P Moridian","year":"2022","unstructured":"Moridian P, Shoeibi A, Khodatars M, Jafari M, Pachori RB, Khadem A, Alizadehsani R, Ling SH (2022) Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: methods, challenges, and future works. WIREs Data Min Knowl Discov 12(6):e1478","journal-title":"WIREs Data Min Knowl Discov"},{"issue":"7","key":"9808_CR49","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.5664\/jcsm.10532","volume":"19","author":"G Bazoukis","year":"2023","unstructured":"Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA (2023) Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 19(7):1337\u20131363","journal-title":"J Clin Sleep Med"},{"key":"9808_CR50","unstructured":"Li Y, Milling M, Schuller B (2025) Neuroplasticity in artificial intelligence\u2014An overview and inspirations on drop in & out learning. ArXiv. ;2503.21419.\\"},{"key":"9808_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07229-3","volume":"9","author":"JB Stephansen","year":"2018","unstructured":"Stephansen JB, Olesen AN, Olsen M et al (2018) Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun 9:5229","journal-title":"Nat Commun"},{"key":"9808_CR52","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning. MIT Press, Cambridge. Nov 18"},{"issue":"3","key":"9808_CR53","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Markets 31(3):685\u2013695","journal-title":"Electron Markets"},{"key":"9808_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107419","volume":"165","author":"F Vaquerizo-Villar","year":"2023","unstructured":"Vaquerizo-Villar F, Guti\u00e9rrez-Tobal GC, Calvo E, \u00c1lvarez D, Kheirandish-Gozal L, Del Campo F, Gozal D, Hornero R (2023) An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea. Comput Biol Med 165:107419","journal-title":"Comput Biol Med"},{"key":"9808_CR55","unstructured":"Akter S, Masud MA, Promi M, Sultana N, Ahmed M, Rahman MM, Yousuf MA, Aloteibi S, Moni MA. Dream: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead Ecg Signals. Mahmudur and Yousuf, Mohammad Abu and Aloteibi, Saad and Moni, Mohammad Ali, Dream: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead Ecg Signals"}],"container-title":["European Archives of Oto-Rhino-Laryngology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-025-09808-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00405-025-09808-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00405-025-09808-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:02:56Z","timestamp":1775484176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00405-025-09808-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["9808"],"URL":"https:\/\/doi.org\/10.1007\/s00405-025-09808-9","relation":{},"ISSN":["0937-4477","1434-4726"],"issn-type":[{"value":"0937-4477","type":"print"},{"value":"1434-4726","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"25 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The word \u201cbayesian\u201d in the title of the article should be \u201cBayesian\u201d.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"RS, EYG, JOZR had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Responsibility"}},{"value":"The authors have no conflicts of interest to declare.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}