{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T16:48:03Z","timestamp":1794242883786,"version":"4.9.0"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","heartandlung.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Heart & Lung"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.hrtlng.2025.05.003","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T03:13:51Z","timestamp":1747970031000},"page":"139-152","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review"],"prefix":"10.1016","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9794-4507","authenticated-orcid":false,"given":"Javier","family":"Mu\u00f1oz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roc\u00edo","family":"Ru\u00edz-Cacho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nerio Jos\u00e9","family":"Fern\u00e1ndez-Araujo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Candela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lourdes Carmen","family":"Visedo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Mu\u00f1oz-Visedo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.hrtlng.2025.05.003_bib0001","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1016\/j.jcrc.2015.07.007","article-title":"Epidemiological trends in invasive mechanical ventilation in the United States: a population-based study","volume":"30","author":"Mehta","year":"2015","journal-title":"J Crit Care"},{"issue":"10","key":"10.1016\/j.hrtlng.2025.05.003_bib0002","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s00134-006-0301-8","article-title":"Patient-ventilator asynchrony during assisted mechanical ventilation","volume":"32","author":"Thille","year":"2006","journal-title":"Intensive Care Med"},{"issue":"7\u2013S","key":"10.1016\/j.hrtlng.2025.05.003_bib0003","first-page":"6","article-title":"Patient-ventilator asynchronies: types, outcomes and nursing detection skills","volume":"89","author":"Bulleri","year":"2018","journal-title":"Acta Biomed"},{"issue":"5","key":"10.1016\/j.hrtlng.2025.05.003_bib0004","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.hrtlng.2020.04.002","article-title":"Patient-ventilator asynchrony in critical care settings: national outcomes of ventilator waveform analysis","volume":"49","author":"Alqahtani","year":"2020","journal-title":"Heart & Lung"},{"issue":"3","key":"10.1016\/j.hrtlng.2025.05.003_bib0005","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.hrtlng.2014.02.002","article-title":"Patient ventilator asynchrony in critically ill adults: frequency and types","volume":"43","author":"Mellott","year":"2014","journal-title":"Heart & Lung"},{"issue":"9","key":"10.1016\/j.hrtlng.2025.05.003_bib0006","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1164\/rccm.201212-2214CI","article-title":"Patient\u2013Ventilator interactions. Implications for clinical management","volume":"188","author":"Gilstrap","year":"2013","journal-title":"Am J Respir Crit Care Med"},{"issue":"4","key":"10.1016\/j.hrtlng.2025.05.003_bib0007","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s00134-015-3692-6","article-title":"Asynchronies during mechanical ventilation are associated with mortality","volume":"41","author":"Blanch","year":"2015","journal-title":"Intensive Care Med"},{"issue":"S1","key":"10.1016\/j.hrtlng.2025.05.003_bib0008","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s40635-019-0234-5","article-title":"Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities","volume":"7","author":"De Haro","year":"2019","journal-title":"ICMx"},{"issue":"11","key":"10.1016\/j.hrtlng.2025.05.003_bib0009","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1097\/CCM.0b013e318225753c","article-title":"Efficacy of ventilator waveforms observation in detecting patient\u2013ventilator asynchrony*","volume":"39","author":"Colombo","year":"2011","journal-title":"Crit Care Med"},{"issue":"2","key":"10.1016\/j.hrtlng.2025.05.003_bib0010","first-page":"202","article-title":"Using ventilator graphics to identify patient-ventilator asynchrony","volume":"50","author":"Nilsestuen","year":"2005","journal-title":"Respir Care"},{"issue":"1","key":"10.1016\/j.hrtlng.2025.05.003_bib0011","doi-asserted-by":"crossref","first-page":"61","DOI":"10.4187\/respcare.01077","article-title":"Monitoring of patient-ventilator interaction at the bedside","volume":"56","author":"De Wit","year":"2011","journal-title":"Respir Care"},{"issue":"1","key":"10.1016\/j.hrtlng.2025.05.003_bib0012","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s13054-023-04414-9","article-title":"Impact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study)","volume":"27","author":"Silva","year":"2023","journal-title":"Crit Care"},{"issue":"3","key":"10.1016\/j.hrtlng.2025.05.003_bib0013","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1097\/MCC.0000000000000307","article-title":"Monitoring patient\u2013ventilator asynchrony:","volume":"22","author":"Dres","year":"2016","journal-title":"curr Opin Crit Care"},{"issue":"4","key":"10.1016\/j.hrtlng.2025.05.003_bib0014","doi-asserted-by":"crossref","first-page":"464","DOI":"10.4187\/respcare.05949","article-title":"Minimizing asynchronies in mechanical ventilation: current and future trends","volume":"63","author":"Subir\u00e0","year":"2018","journal-title":"Respir Care"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0015","series-title":"B38. OPTIMIZING MECHANICAL VENTILATION AND VENTILATOR LIBERATION","first-page":"A3435","article-title":"Artificial intelligence models in the detection of patient ventilator asynchrony: a review of techniques and clinical targets","author":"Tlimat","year":"2024"},{"issue":"3","key":"10.1016\/j.hrtlng.2025.05.003_bib0016","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","article-title":"Explaining Deep Neural Networks and beyond: a review of methods and applications","volume":"109","author":"Samek","year":"2021","journal-title":"Proc IEEE"},{"issue":"7","key":"10.1016\/j.hrtlng.2025.05.003_bib0018","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0019","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Vol 54. Proceedings of Machine Learning Research","first-page":"623","article-title":"Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers","author":"Kull","year":"2017"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0020","series-title":"Big Data, Big Challenges: A Healthcare Perspective: Background, Issues, Solutions and Research Directions","author":"Mowafa","year":"2017"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0021","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt","year":"2000","journal-title":"Adv Large Margin Classif"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108349","article-title":"Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model","volume":"173","author":"Agrawal","year":"2024","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.ifacsc.2024.100266","article-title":"Exploring variable observational time windows for patient\u2013ventilator asynchrony during mechanical ventilation treatment","volume":"29","author":"Ang","year":"2024","journal-title":"IFAC Journal of Systems and Control"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108323","article-title":"Patient-ventilator asynchrony classification in mechanically ventilated patients: model-based or machine learning method?","volume":"255","author":"Ang","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105103","article-title":"Patient asynchrony modelling during controlled mechanical ventilation therapy","volume":"183","author":"Arunachalam","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcrc.2023.154256","article-title":"Reverse triggering neural network and rules-based automated detection in acute respiratory distress syndrome","volume":"75","author":"Baedorf-Kassis","year":"2023","journal-title":"J Crit Care"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0027","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"150","article-title":"A machine learning method for automatic detection and classification of patient-ventilator asynchrony","author":"Bakkes","year":"2020"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107333","article-title":"Automated detection and classification of patient\u2013ventilator asynchrony by means of machine learning and simulated data","volume":"230","author":"Bakkes","year":"2023","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"10.1016\/j.hrtlng.2025.05.003_bib0029","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1007\/s00134-012-2493-4","article-title":"Validation of the Better Care\u00ae system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study","volume":"38","author":"Blanch","year":"2012","journal-title":"Intensive Care Med"},{"issue":"2","key":"10.1016\/j.hrtlng.2025.05.003_bib0030","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10877-020-00469-z","article-title":"An effective pressure\u2013flow characterization of respiratory asynchronies in mechanical ventilation","volume":"35","author":"Casagrande","year":"2021","journal-title":"J Clin Monit Comput"},{"issue":"13","key":"10.1016\/j.hrtlng.2025.05.003_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e33692","article-title":"Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit","volume":"10","author":"Chen","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0032","doi-asserted-by":"crossref","first-page":"653","DOI":"10.2147\/TCRM.S482662","article-title":"Performance of machine learning algorithms in predicting prolonged mechanical ventilation in patients with blunt chest trauma","volume":"20","author":"Chen","year":"2024","journal-title":"Ther Clin Risk Manage"},{"issue":"15","key":"10.1016\/j.hrtlng.2025.05.003_bib0033","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.ifacol.2021.10.276","article-title":"Classification patient-ventilator asynchrony with dual-input convolutional neural network","volume":"54","author":"Chong","year":"2021","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0034","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.compbiomed.2018.04.016","article-title":"Replicating human expertise of mechanical ventilation waveform analysis in detecting patient-ventilator cycling asynchrony using machine learning","volume":"97","author":"Gholami","year":"2018","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108175","article-title":"An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection","volume":"250","author":"Hao","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"issue":"24","key":"10.1016\/j.hrtlng.2025.05.003_bib0036","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.ifacol.2024.11.088","article-title":"Mechanical ventilation mode Classification: a dual-input convolutional neural network approach with class activation mapping","volume":"58","author":"Hor","year":"2024","journal-title":"IFAC-PapersOnLine"},{"issue":"27","key":"10.1016\/j.hrtlng.2025.05.003_bib0037","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ifacol.2018.11.610","article-title":"A machine learning model for real-time asynchronous breathing monitoring","volume":"51","author":"Loo","year":"2018","journal-title":"IFAC-PapersOnLine"},{"issue":"2","key":"10.1016\/j.hrtlng.2025.05.003_bib0038","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1016\/j.ifacol.2023.10.1106","article-title":"Patient-ventilator interaction using autoencoder derived magnitude of asynchrony breathing","volume":"56","author":"Loo","year":"2023","journal-title":"IFAC-PapersOnLine"},{"issue":"1","key":"10.1016\/j.hrtlng.2025.05.003_bib0039","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-36011-0","article-title":"Predicting patient-ventilator asynchronies with hidden Markov models","volume":"8","author":"Marchuk","year":"2018","journal-title":"Sci Rep"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105251","article-title":"A novel application of spectrograms with machine learning can detect patient ventilator dyssynchrony","volume":"86","author":"Obeso","year":"2023","journal-title":"Biomed Signal Process Control"},{"issue":"12","key":"10.1016\/j.hrtlng.2025.05.003_bib0041","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.3390\/s21124149","article-title":"Identifying patient\u2013Ventilator asynchrony on a small dataset using image-based transfer learning","volume":"21","author":"Pan","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106057","article-title":"An interpreTable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation","volume":"204","author":"Pan","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103170","article-title":"Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning","volume":"71","author":"Radhakrishnan","year":"2022","journal-title":"Biomed Signal Process Control"},{"issue":"04","key":"10.1016\/j.hrtlng.2025.05.003_bib0044","doi-asserted-by":"crossref","first-page":"208","DOI":"10.3414\/ME17-02-0012","article-title":"Creation of a robust and generalizable machine learning classifier for patient ventilator asynchrony","volume":"57","author":"Rehm","year":"2018","journal-title":"Methods Inf Med"},{"issue":"5","key":"10.1016\/j.hrtlng.2025.05.003_bib0045","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1097\/CCM.0000000000006180","article-title":"Flow-limited and reverse-triggered ventilator dyssynchrony are associated with increased tidal and dynamic transpulmonary pressure*","volume":"52","author":"Sottile","year":"2024","journal-title":"Crit Care Med"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.ifacsc.2023.100236","article-title":"Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions","volume":"27","author":"Van De Kamp","year":"2024","journal-title":"IFAC Journal of Systems and Control"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103721","article-title":"Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network","volume":"120","author":"Zhang","year":"2020","journal-title":"Comput Biol Med"},{"issue":"11","key":"10.1016\/j.hrtlng.2025.05.003_bib0053","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1164\/rccm.201212-2311ED","article-title":"Big data\u201d in the intensive care unit. Closing the data loop","volume":"187","author":"Celi","year":"2013","journal-title":"Am J Respir Crit Care Med"},{"issue":"1","key":"10.1016\/j.hrtlng.2025.05.003_bib0054","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41591-018-0272-7","article-title":"Privacy in the age of medical big data","volume":"25","author":"Price","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.hrtlng.2025.05.003_bib0055","series-title":"Artificial Intelligence in Healthcare","first-page":"295","article-title":"Ethical and legal challenges of artificial intelligence-driven healthcare","author":"Gerke","year":"2020"}],"container-title":["Heart & Lung"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0147956325001116?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0147956325001116?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:14:27Z","timestamp":1750896867000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0147956325001116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":49,"alternative-id":["S0147956325001116"],"URL":"https:\/\/doi.org\/10.1016\/j.hrtlng.2025.05.003","relation":{},"ISSN":["0147-9563"],"issn-type":[{"value":"0147-9563","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review","name":"articletitle","label":"Article Title"},{"value":"Heart & Lung","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hrtlng.2025.05.003","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}