{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T22:30:36Z","timestamp":1790807436928,"version":"4.1.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Respiratory Care"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>Patient\u2013ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient respiratory effort. PVA is highly prevalent and associated with adverse clinical outcomes, including increased work of breathing, oxygen consumption, and risk of barotrauma. Artificial intelligence (AI) is a potentially transformative solution offering capabilities for automated detection of PVA. This narrative review characterizes the landscape of AI models designed for PVA detection and quantification. A comprehensive literature search identified 13 studies, spanning diverse settings and patient populations. Machine learning (ML) techniques, derivation datasets, types of asynchronies detected, and performance metrics were assessed to provide a contemporary view of AI in this domain. We reviewed 166 articles published between 1989 and April 2024, of which 13 were included, encompassing 332 participants and analyzing &gt;5.8 million breaths. Patient counts ranged between 8 and 107 and breath data ranged between 1,375 and 4.2 M. The reason for invasive mechanical ventilation use was given as ARDS in three articles, whereas the remainder had different invasive mechanical ventilation indications. Various ML methods as well as newer deep learning techniques were used to address PVA types. Sensitivity and specificity of 10 of the 13 models were &gt;0.9, and 8 models reported accuracy of &gt;0.9. AI models have significant potential to address PVA in invasive mechanical ventilation, displaying high accuracy across various populations and asynchrony types. This showcases their potential to accurately detect and quantify PVA. Future work should focus on model validation in diverse clinical settings and patient populations.<\/jats:p>","DOI":"10.1089\/respcare.12540","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T11:59:07Z","timestamp":1743681547000},"page":"583-592","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial Intelligence for the Detection of Patient\u2013Ventilator Asynchrony"],"prefix":"10.1177","volume":"70","author":[{"given":"Abdulhakim","family":"Tlimat","sequence":"first","affiliation":[{"name":"Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cosmo","family":"Fowler","sequence":"additional","affiliation":[{"name":"Dr. Fowler is affiliated with the Division of Pulmonary, Allergy, and Critical Care, and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Safadi","sequence":"additional","affiliation":[{"name":"Dr. Safadi is affiliated with the Division of Nephrology and Hypertension, University of Minnesota, Minneapolis, Minnesota, USA."},{"name":"Dr. Safadi is affiliated with the Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Minnesota, Minneapolis, Minnesota, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert B.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Mr. Johnson is affiliated with the Respiratory Therapy Department, The University of Alabama Medical Center, University of Alabama at Birmingham, Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep","family":"Bodduluri","sequence":"additional","affiliation":[{"name":"Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA."},{"name":"Drs. Bodduluri and Bhatt are affiliated with Center for Lung Analytics and Imaging Research (CLAIR), Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Morris","sequence":"additional","affiliation":[{"name":"Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surya P.","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA."},{"name":"Drs. Bodduluri and Bhatt are affiliated with Center for Lung Analytics and Imaging Research (CLAIR), Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,5,5]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201212-2214CI"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40635-019-0234-5"},{"issue":"19","key":"e_1_3_4_4_2","article-title":"Patient-ventilator dyssynchrony in critically ill patients","volume":"10","author":"De Oliveira B","year":"2021","unstructured":"De Oliveira B, , Aljaberi N, , Taha A, et al. Patient-ventilator dyssynchrony in critically ill patients. 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