Prediction of Difficult Tracheal Intubation by Artificial Intelligence: A Prospective Observational Study

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dc.contributor.authorÇelik, Fatma
dc.contributor.authorAydemir, Emrah
dc.date.accessioned2023-04-10T20:19:34Z
dc.date.available2023-04-10T20:19:34Z
dc.date.issued2021
dc.departmentRektörlük, Rektörlüğe Bağlı Birimler, Düzce Üniversitesi Dergilerien_US
dc.description.abstractAim: Many predictive clinical tests are used together for preoperative detection of patientswith difficult airway risk. In this study, we aimed to predict difficult intubation with differentartificial intelligence algorithms using various clinical tests and anthropometric measurements,besides, to evaluate the accuracy performance of Cormack and Lehane (C-L) classificationwith artificial intelligence.Material and Methods: This study was conducted as a single-blind prospective observationalstudy between 2016 and 2019. A total of 1486 patients with American Society ofAnesthesiologists physical status I-III, scheduled to undergo elective surgery and requiringendotracheal intubation, were included. Demographic variables, clinical tests andanthropometric measurements of the patients were recorded. Difficult intubation was evaluatedusing the 4-grade C-L system according to the easy and difficult intubation criteria. Difficultintubation was tried to predict using 16 different artificial intelligence algorithms.Results: The highest success rate among artificial intelligence algorithms was obtained by theRandomForest method. With this method, difficult intubation was predicted with 92.85%sensitivity, 96.94% specificity, 93.69% positive predictive value and 96.52% negativepredictive value. C-L classification accuracy performance also determined as 95.60%.Conclusion: Artificial intelligence has been considerably successful in predicting difficultintubation. Besides, C-L classifications of easy and difficult intubated patients weresuccessfully predicted with artificial intelligence algorithms. Using a 6-grade modified C-Lclassification for laryngeal view may provide stronger difficult intubation prediction. A saferand more potent prediction in training artificial intelligence can be achieved by addingindividual differences and clinical features that support the definition of difficult intubation.en_US
dc.identifier.doi10.18678/dtfd.862467
dc.identifier.endpage54en_US
dc.identifier.issn1307-671X
dc.identifier.issue1en_US
dc.identifier.startpage47en_US
dc.identifier.trdizinid421872en_US
dc.identifier.urihttp://doi.org/10.18678/dtfd.862467
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/421872
dc.identifier.urihttps://hdl.handle.net/20.500.12684/11420
dc.identifier.volume23en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofDüzce Tıp Fakültesi Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titlePrediction of Difficult Tracheal Intubation by Artificial Intelligence: A Prospective Observational Studyen_US
dc.typeArticleen_US

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