Wu Jinze, Yao Yuan, Zhang Guangchao, Li Xiaofan, Peng Bo
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.
General Practice Medical Center, West China Hospital, Sichuan University, Chengdu 610044, China.
Bioengineering (Basel). 2024 Jul 11;11(7):703. doi: 10.3390/bioengineering11070703.
The preoperative assessment of difficult airways is of great significance in the practice of anesthesia intubation. In recent years, although a large number of difficult airway recognition algorithms have been investigated, defects such as low recognition accuracy and poor recognition reliability still exist. In this paper, we propose a Dual-Path Multi-View Fusion Network (DMF-Net) based on multi-view metric learning, which aims to predict difficult airways through multi-view facial images of patients. DMF-Net adopts a dual-path structure to extract features by grouping the frontal and lateral images of the patients. Meanwhile, a Multi-Scale Feature Fusion Module and a Hybrid Co-Attention Module are designed to improve the feature representation ability of the model. Consistency loss and complementarity loss are utilized fully for the complementarity and consistency of information between multi-view data. Combined with Focal Loss, information bias is effectively avoided. Experimental validation illustrates the effectiveness of the proposed method, with the accuracy, specificity, sensitivity, and score reaching 77.92%, 75.62%, 82.50%, and 71.35%, respectively. Compared with methods such as clinical bedside screening tests and existing artificial intelligence-based methods, our method is more accurate and reliable and can provide a reliable auxiliary tool for clinical healthcare personnel to effectively improve the accuracy and reliability of preoperative difficult airway assessments. The proposed network can help to identify and assess the risk of difficult airways in patients before surgery and reduce the incidence of postoperative complications.
困难气道的术前评估在麻醉插管实践中具有重要意义。近年来，尽管已研究了大量困难气道识别算法，但仍存在识别准确率低和识别可靠性差等缺陷。本文提出一种基于多视图度量学习的双路径多视图融合网络（DMF-Net），旨在通过患者的多视图面部图像预测困难气道。DMF-Net采用双路径结构，通过对患者的正面和侧面图像进行分组来提取特征。同时，设计了多尺度特征融合模块和混合协同注意力模块以提高模型的特征表示能力。充分利用一致性损失和互补性损失来实现多视图数据之间信息的互补性和一致性。结合焦点损失，有效避免了信息偏差。实验验证表明了所提方法的有效性，准确率、特异性、灵敏度和得分分别达到77.92%、75.62%、82.50%和71.35%。与临床床边筛查测试和现有的基于人工智能的方法等相比，我们的方法更准确、可靠，可为临床医护人员提供可靠的辅助工具，有效提高术前困难气道评估的准确性和可靠性。所提网络有助于在术前识别和评估患者困难气道风险，并降低术后并发症的发生率。
