ArticleFrontiers in neurology
Machine learning-based prediction of difficult laryngoscopy in infants with Pierre Robin sequence using quantitative 3D computed tomography parameters.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Infants with Pierre Robin sequence (PRS) frequently present with difficult laryngoscopic exposure due to mandibular hypoplasia, glossoptosis, and upper airway narrowing. Quantitative three-dimensional computed tomography (3D-CT) enables objective characterization of airway anatomy and may improve preoperative airway assessment. This study aimed to identify key 3D-CT parameters associated with difficult laryngoscopic exposure in infants with PRS and to develop and externally validate machine learning-based predictive models. Methods: We retrospectively analyzed 214 infants with PRS who underwent mandibular distraction osteogenesis between 2023 and 2024. According to laryngoscopic view, patients were classified into an easy-exposure group (Cormack-Lehane grades I-II) and a difficult-exposure group (grades III-IV). Univariable and multivariable logistic regression analyses were performed to identify independent quantitative 3D-CT predictors. Seven machine learning models were constructed, including logistic regression, support vector machine, random forest, Extra Trees, XGBoost, LightGBM, and AdaBoost. Internal performance was evaluated using five-fold cross-validation, and generalizability was assessed in an independent temporal validation cohort from the same institution ( Results: Multivariable analysis identified four independent predictors: tongue length (D1) (odds ratio [OR] = 1.058, Conclusion: Quantitative 3D-CT parameters reflecting tongue morphology and oropharyngeal airway dimensions are clinically relevant predictors of difficult laryngoscopic exposure in infants with PRS. The Extra Trees model showed promising performance in temporal validation within a single-center cohort.
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