ArticleFrontiers in public health2022
Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment.World journal of radiology · 2026Review
- Pre-operative prediction of difficult tracheal intubation using an explainable multi-pose shared encoder vision transformer: a prospective observational study.BMC anesthesiology · 2026Observational
- Predicting Difficult Tracheal Intubation Using Multi-Angle Photographic Analysis with Convolutional Neural Networks and EfficientNet.Diagnostics (Basel, Switzerland) · 2026Article
- AI-based decision models for difficult airway assessment: from research innovation to clinical implementation-a narrative review.Frontiers in medicine · 2026Review
- Evaluation of anthropometric and ultrasonographic measurements with different machine learning methods in predicting difficult intubation: a prospective observational study.BMC anesthesiology · 2025Observational
- Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.BMJ open · 2025Article
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.BMC anesthesiology · 2024Article
- Factors predicting the use of the backward upward rightward pressure maneuver in thyroid surgery: a single-center retrospective cohort study.BMC surgery · 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 predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.Scientific reports · 2024Article
- Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery.Heliyon · 2024Article
- A review of the current status and progress in difficult airway assessment research.European journal of medical research · 2024Review
- Scope of artificial intelligence in airway management.Indian journal of anaesthesia · 2024Article
- On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology.Current anesthesiology reports · 2023Article
- Endotracheal Intubation of Difficult Airways in Emergency Settings: A Guide for Innovators.Medical devices (Auckland, N.Z.) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery. Methods: We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python. Results: The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%. Conclusion: According to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.
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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.