AdaDenseNet-LUC: Adaptive Attention DenseNet for Laryngeal Ultrasound Image Classification

Evaluating the difficulty of endotracheal intubation during pre-anesthesia assessment has consistently posed a challenge for clinicians. Accurate prediction of intubation difficulty is crucial for subsequent treatment planning. However, existing diagnostic methods often suffer from low accuracy. To tackle this issue, this study presented an automated airway classification method utilizing Convolutional Neural Networks (CNNs). We proposed Adaptive Attention DenseNet for Laryngeal Ultrasound Classification (AdaDenseNet-LUC), a network architecture that enhances classification performance by integrating an adaptive attention mechanism into DenseNet (Dense Convolutional Network), enabling the extraction of deep features that aid in difficult airway classification. This model associates laryngeal ultrasound images with actual intubation difficulty, providing healthcare professionals with scientific evidence to help improve the accuracy of clinical decision-making. Experiments were performed on a dataset of 1391 ultrasound images, utilizing 5-fold cross-validation to assess the model’s performance. The experimental results show that the proposed method achieves a classification accuracy of 87.41%, sensitivity of 86.05%, specificity of 88.59%, F1 score of 0.8638, and AUC of 0.94. Grad-CAM visualization techniques indicate that the model’s attention is attention to the tracheal region. The results demonstrate that the proposed method outperforms current approaches, delivering objective and accurate airway classification outcomes, which serve as a valuable reference for evaluating the difficulty of endotracheal intubation and providing guidance for clinicians.