{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:15:03Z","timestamp":1787318103483,"version":"build-2736575974"},"reference-count":29,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Anaesthesia Critical Care &amp; Pain Medicine"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.accpm.2025.101589","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T21:15:35Z","timestamp":1752614135000},"page":"101589","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Artificial intelligence in airway management: A systematic review and meta-analysis"],"prefix":"10.1016","volume":"44","author":[{"given":"Mohamed F.","family":"Abosamak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hany A.","family":"Zaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eman E.","family":"Shaban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amira","family":"Shaban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Shaban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitham","family":"Hodhod","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benny","family":"Ponappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.accpm.2025.101589_bib0005","first-page":"456","article-title":"Recent advances in airway management","volume":"63","author":"Myatra","year":"2019","journal-title":"Indian J. Anaesth"},{"key":"10.1016\/j.accpm.2025.101589_bib0010","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1097\/ALN.0000000000004002","article-title":"2022 American Society of Anesthesiologists Practice Guidelines for Management of the Difficult Airway*","volume":"136","author":"Apfelbaum","year":"2022","journal-title":"Anesthesiology"},{"key":"10.1016\/j.accpm.2025.101589_bib0015","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1186\/s12871-024-02627-1","article-title":"Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis","volume":"24","author":"Wang","year":"2024","journal-title":"BMC Anesthesiol"},{"key":"10.1016\/j.accpm.2025.101589_bib0020","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1186\/s40001-024-01759-x","article-title":"A review of the current status and progress in difficult airway assessment research","volume":"29","author":"Chen","year":"2024","journal-title":"Eur J Med Res"},{"key":"10.1016\/j.accpm.2025.101589_bib0025","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1111\/anae.15820","article-title":"Analysis of a national difficult airway database","volume":"77","author":"Sajayan","year":"2022","journal-title":"Anaesthesia"},{"key":"10.1016\/j.accpm.2025.101589_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bja\/aew148","article-title":"Prediction in airway management: what is worthwhile, what is a waste of time and what about the future?","volume":"117","author":"Teoh","year":"2016","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.accpm.2025.101589_bib0035","doi-asserted-by":"crossref","DOI":"10.21037\/fomm-20-79","article-title":"Advances in studies on imaging and artificial intelligence technology-assisted difficult airway assessment","volume":"3","author":"Wang","year":"2021","journal-title":"Front Oral Maxillofac Med"},{"key":"10.1016\/j.accpm.2025.101589_bib0040","article-title":"Airway physical examination tests for detection of difficult airway management in apparently normal adult patients","volume":"5","author":"Roth","year":"2018","journal-title":"Cochrane Database Syst Rev"},{"issue":"2","key":"10.1016\/j.accpm.2025.101589_bib0045","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1213\/ANE.0000000000006969","article-title":"The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management","volume":"140","author":"De Rosa","year":"2025","journal-title":"Anesth Analg"},{"key":"10.1016\/j.accpm.2025.101589_bib0050","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.3390\/jcm14051602","article-title":"Use of artificial intelligence in difficult airway assessment: the current state of knowledge","volume":"14","author":"Wilk","year":"2025","journal-title":"J Clin Med"},{"key":"10.1016\/j.accpm.2025.101589_bib0055","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"10.1016\/j.accpm.2025.101589_bib0060","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.accpm.2025.101589_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2022.e11761","article-title":"Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods","volume":"8","author":"Liu","year":"2022","journal-title":"Heliyon"},{"key":"10.1016\/j.accpm.2025.101589_bib0070","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1111\/anae.16194","article-title":"Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study","volume":"79","author":"Xia","year":"2024","journal-title":"Anaesthesia"},{"key":"10.1016\/j.accpm.2025.101589_bib0075","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s12873-025-01185-0","article-title":"Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method","volume":"25","author":"Srivilaithon","year":"2025","journal-title":"BMC Emerg Med"},{"key":"10.1016\/j.accpm.2025.101589_bib0080","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1111\/anae.15874","article-title":"Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view","volume":"78","author":"Cho","year":"2023","journal-title":"Anaesthesia"},{"key":"10.1016\/j.accpm.2025.101589_bib0085","doi-asserted-by":"crossref","DOI":"10.2196\/28366","article-title":"Machine learning approaches for predicting difficult airway and first-pass success in the emergency department: multicenter prospective observational study","volume":"11","author":"Yamanaka","year":"2022","journal-title":"Interact J Med Res"},{"key":"10.1016\/j.accpm.2025.101589_bib0090","first-page":"622","article-title":"Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study","volume":"72","author":"Kim","year":"2022","journal-title":"Braz J Anesthesiol"},{"key":"10.1016\/j.accpm.2025.101589_bib0095","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/s12871-021-01343-4","article-title":"Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height","volume":"21","author":"Kim","year":"2021","journal-title":"BMC Anesthesiol"},{"key":"10.1016\/j.accpm.2025.101589_bib0100","doi-asserted-by":"crossref","first-page":"14209","DOI":"10.1038\/s41598-024-65060-x","article-title":"Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study","volume":"14","author":"Kim","year":"2024","journal-title":"Sci Rep"},{"key":"10.1016\/j.accpm.2025.101589_bib0105","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s40560-021-00551-x","article-title":"Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study","volume":"9","author":"Hayasaka","year":"2021","journal-title":"J Intensive Care"},{"key":"10.1016\/j.accpm.2025.101589_bib0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108118","article-title":"Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods","volume":"248","author":"Garc\u00eda-Garc\u00eda","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.accpm.2025.101589_bib0115","doi-asserted-by":"crossref","first-page":"21","DOI":"10.58888\/2957-3912-2023-03-03","article-title":"Comparison of machine learning models for difficult airway","volume":"2","author":"Wang","year":"2023","journal-title":"J Anesth Transl Med"},{"key":"10.1016\/j.accpm.2025.101589_bib0120","article-title":"A fully-automatic semi-supervised deep learning model for difficult airway assessment","volume":"9","author":"Wang","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.accpm.2025.101589_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104737","article-title":"Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models","volume":"136","author":"Tavolara","year":"2021","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.accpm.2025.101589_bib0130","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.accpm.2025.101589_bib0135","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1002\/cac2.12012","article-title":"Emerging role of deep learning-based artificial intelligence in tumor pathology","volume":"40","author":"Jiang","year":"2020","journal-title":"Cancer Commun"},{"key":"10.1016\/j.accpm.2025.101589_bib0140","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.accpm.2025.101589_bib0145","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00134-019-05872-y","article-title":"Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy","volume":"46","author":"Fleuren","year":"2020","journal-title":"Intensive Care Med"}],"container-title":["Anaesthesia Critical Care &amp; Pain Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352556825001213?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352556825001213?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:31:15Z","timestamp":1765888275000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352556825001213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["S2352556825001213"],"URL":"https:\/\/doi.org\/10.1016\/j.accpm.2025.101589","relation":{},"ISSN":["2352-5568"],"issn-type":[{"value":"2352-5568","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in airway management: A systematic review and meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Anaesthesia Critical Care & Pain Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.accpm.2025.101589","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Soci\u00e9t\u00e9 Fran\u00e7aise d'Anesth\u00e9sie et de R\u00e9animation (SFAR). Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101589"}}