Whenever a patient needs to enter the operating room, in case the surgery requires general anesthesia, he/she must be intubated, and an anesthesiologist has to make a previous check to the patient in order to evaluate his/her airway. This process should be done to the patient to anticipate any problem, such as a difficult airway at the time of being anesthetized. In fact, the inadequate detection of a difficult airway can cause serious complications, even death. This research work proposes a mobile app that uses a convolutional neural network to detect a difficult airway. This model classifies two classes of the Mallampati score, namely Mallampati 1–2 (with low risk of difficult airway) and Mallampati 3–4 (with higher risk of difficult airway). The average accuracy of the predictive model is 88.5% for classifying pictures. A total of 240 pictures were used for training the model. The results of sensitivity and specificity were 90% in average.

中文翻译：

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使用深度学习检测困难气道

每当患者需要进入手术室时，如果手术需要全身麻醉，则必须给他/她插管，并且麻醉师必须事先对患者进行检查以评估他/她的呼吸道。应该对患者进行此过程以预料到任何问题，例如在麻醉时出现气道困难。实际上，对困难气道的检测不足会导致严重的并发症，甚至死亡。这项研究工作提出了一个使用卷积神经网络来检测困难气道的移动应用程序。该模型将Mallampati得分分为两类，即Mallampati 1-2（困难气道风险低）和Mallampati 3-4（困难气道风险高）。对图片进行分类的预测模型的平均准确度为88.5％。总共使用了240张图片来训练模型。敏感性和特异性的结果平均为90％。