Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features

Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intu-bation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience. In this paper, we propose a new method for automatic Mallampati classification. Our method extracts deep features that are more favorable for the Mallampati classification task by introducing an attention mech-anism into the basic deep convolutional neural network (DCNN) and then further improves the classification performance by jointly using attention-based deep features with handcrafted features. We conducted experi-ments on a dataset consisting of 321 oral images collected online. The proposed method has a classification accuracy of 97.50%, a sensitivity of 96.52%, a specificity of 98.05%, and an F1 score of 96.52% after five-fold cross-validation. The experimental results show that our proposed method is superior to other methods, can assist doctors in determining Mallampati class objectively and accurately, and provide an essential reference element for assessing the difficulty of tracheal intubation.

Automated summary

Preoperative assessment of tracheal intubation difficulty is crucial in anesthesia practice. Current AI methods for Mallampati classification are unreliable, relying solely on doctors' experience. This study proposes a new automatic Mallampati classification method that combines deep features and handcrafted features to improve the accuracy of difficulty assessment in tracheal intubation.