ArticleBMC anesthesiology2021
Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.
Article in BMC anesthesiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- The applications of ChatGPT and other large language models in anesthesiology and critical care: a systematic review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Pooled it
- Intelligent Recognition Network for Difficult Airway Based on Multi-View Input and Attention Aggregation.Interdisciplinary sciences, computational life sciences · 2026Article
- Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.Journal of anesthesia, analgesia and critical care · 2026Review
- Article
- AI-based decision models for difficult airway assessment: from research innovation to clinical implementation-a narrative review.Frontiers in medicine · 2026Review
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
- Incidence and predictors of difficult videolaryngoscopy using a hyperangulated device in elective surgical patients: a prospective cohort study in China.BMC anesthesiology · 2025Article
- Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.BMJ open · 2025Article
- Anesthesia depth prediction from drug infusion history using hybrid AI.BMC medical informatics and decision making · 2025Article
- Use of Artificial Intelligence in Difficult Airway Assessment: The Current State of Knowledge.Journal of clinical medicine · 2025Review
- Determination of the Accuracy of Thyromental Height Test for Predicting Difficult Laryngoscopy in Obstetric Patients Who Are Candidates for Cesarean Section: An Observational Study.Current therapeutic research, clinical and experimental · 2025Article
- Magnitude, associated factors of difficult airway, and predictive value of airway examinations among maxillofacial surgery patients at public hospitals in Southern Ethiopia: a multicentre cross-sectional study.Annals of medicine and surgery (2012) · 2024Article
- Machine Learning Predictions and Identifying Key Predictors for Safer Intubation: A Study on Video Laryngoscopy Views.Journal of personalized medicine · 2024Article
- Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study.Scientific reports · 2024Article
- Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.Current oncology (Toronto, Ont.) · 2024Review
- A review of the current status and progress in difficult airway assessment research.European journal of medical research · 2024Review
- ChatGPT and Its Potential Implications for Clinical Practice: An Anesthesiology Perspective.Biomedical instrumentation & technology · 2023Article
- Artificial intelligence and anesthesia: a narrative review.Annals of translational medicine · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation.
methodsVariables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set.
resultsThe model's performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72-0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27-0.37]).
conclusionsMachine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.