ArticleComputers in biology and medicine2021
Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 27 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Comparison of a Computerized Image Analysis to Conventional Airway Examination Techniques to Predict Difficult Endotracheal Intubation
Multimodal Artificial Intelligence for Image-Based Prediction of Difficult Airway: A Prospective Observational Study
Who cites it
27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 64 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
- Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis.BMC anesthesiology · 2024Pooled it
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Predicting difficult laryngoscopy via three-dimensional graphical modeling and hybrid artificial intelligence using standard facial photographs in elective surgery patients.BMC anesthesiology · 2026Observational
- 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
- Establishment an early precise diagnosis model for bone metastasis in lung cancer based on driver gene types.American journal of cancer research · 2026Article
- AI-based decision models for difficult airway assessment: from research innovation to clinical implementation-a narrative review.Frontiers in medicine · 2026Review
- An ultrasound-based artificial intelligence framework for difficult airway prediction: A two-model, three-step decision framework.PloS one · 2026Article
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
- Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.BMJ open · 2025Article
- Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.Perioperative medicine (London, England) · 2025Review
- Use of Artificial Intelligence in Difficult Airway Assessment: The Current State of Knowledge.Journal of clinical medicine · 2025Review
- Artificial intelligence in anesthesiology: a bibliometric analysis.Perioperative medicine (London, England) · 2024Review
- Voice Analysis as a Method for Preoperatively Predicting a Difficult Airway Based on Machine Learning Algorithms: Original Research Report.Health science reports · 2024Article
- Difficult Airway Assessment Based on Multi-View Metric Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study.Scientific reports · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Failure to identify difficult intubation is the leading cause of anesthesia-related death and morbidity. Despite preoperative airway assessment, 75-93% of difficult intubations are unanticipated, and airway examination methods underperform, with sensitivities of 20-62% and specificities of 82-97%. To overcome these impediments, we aim to develop a deep learning model to identify difficult to intubate patients using frontal face images. We proposed an ensemble of convolutional neural networks which leverages a database of celebrity facial images to learn robust features of multiple face regions. This ensemble extracts features from patient images (n = 152) which are subsequently classified by a respective ensemble of attention-based multiple instance learning models. Through majority voting, a patient is classified as difficult or easy to intubate. Whereas two conventional bedside tests resulted in AUCs of 0.6042 and 0.4661, the proposed method resulted in an AUC of 0.7105 using a cohort of 76 difficult and 76 easy to intubate patients. Generic features yielded AUCs of 0.4654-0.6278. The proposed model can operate at high sensitivity and low specificity (0.9079 and 0.4474) or low sensitivity and high specificity (0.3684 and 0.9605). The proposed ensembled model outperforms conventional bedside tests and generic features. Side facial images may improve the performance of the proposed model. The proposed method significantly surpasses conventional bedside tests and deep learning methods. We expect our model will play an important role in developing deep learning methods where frontal face features play an important role.
Indexed as
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What OpenQuestion holds
Registered trials
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.