中圖分類號：TP181；R319 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.268137
中文引用格式： 田伊，陳澤瑋，李百川，等. MMAAF：一種基于多模態視覺特征與機器學習的氣道插管困難評估框架[J]. 電子技術應用，2026，52(6)：13-20.
英文引用格式： Tian Yi，Chen Zewei，Li Baichuan，et al. MMAAF: a multi-modal visual feature and machine learning-based framework for difficult airway intubation assessment[J]. Application of Electronic Technique，2026，52(6)：13-20.

MMAAF： a multi-modal visual feature and machine learning-based framework for difficult airway intubation assessment

Abstract： Difficult airway intubation is a critical risk in clinical anesthesia. This study proposes an automated, objective airway assessment framework named MMAAF (Multi-Modal Airway Assessment Framework) based on static mouth-opening images of patients. Using computer vision techniques, the framework automatically extracts six key anatomical features from the dataset of patient mouth-opening images. These features are combined with actual difficult intubation labels to construct a structured multi-modal prediction dataset, which is processed with a random oversampling technique. The study employs two integrated machine learning algorithms—random forest and gradient boosting decision trees—for model training and evaluation. Results show that the gradient boosting and random forest models achieve prediction accuracies of 92% and 93%, respectively, on an independent test set, demonstrating good assessment performance. Concurrently, a logistic regression model is trained within the MMAAF framework. Features are weighted according to their importance weights, and a personalized probability score for difficult intubation is calculated for each patient. This provides an intuitive quantitative reference based on multi-modal features to support clinical decision-making.

Key words : difficult airway；intubation difficulty prediction；medical image analysis；computer vision；random forest；gradient boosting decision tree