Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation
Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation
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DOI：
10.1109/tbme.2015.2457032
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发表时间：
2016-02-01
影响因子：
4.6
通讯作者：
Thiran, Jean-Philippe
中科院分区：
文献类型：
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作者：
Cuendet, Gabriel Louis;Schoettker, Patrick;Thiran, Jean-Philippe
目的：困难的气管插管是麻醉相关损伤的主要原因，具有潜在的危及生命的并发症。因此，术前困难气道的检测和预测对患者的安全至关重要。我们提出了一种自动人脸分析方法来检测与插管困难相关的形态特征，并改进其预测。方法：为此，我们收集了970名患者的数据库，包括照片、视频和地面实况数据。使用我们的数据库中的人脸学习了特定的统计人脸模型，提供了面部形态的自动参数化。通过随机森林算法提供的重要性排序来选择最具区分性的形态特征。随机森林方法也被用来训练关于这些选定特征的分类器。我们比较了基于类先验的阈值调整方法和两种方法，这两种方法在训练集上学习最优阈值来解决数据库固有的不平衡性质。结果：我们的全自动化方法在简化的实验设置中实现了81.0%的AUC，其中只考虑了容易和困难的患者。在整个数据库上的进一步验证证明，该方法适用于实际困难的插管预测，AUC=77.9%。结论：该系统的性能符合最先进的医学诊断，基于训练有素的麻醉师提供的评级，他们的评估是由一套广泛的标准指导的。意义：我们提出了第一个完全自动和非侵入性的困难插管检测系统，适合于临床使用。
Goal: Difficult tracheal intubation is a major cause of anesthesia-related injuries with potential life threatening complications. Detection and anticipation of difficult airway in the preoperative period is, thus, crucial for the patients' safety. We propose an automatic face-analysis approach to detect morphological traits related to difficult intubation and improve its prediction. Methods: For this purpose, we have collected a database of 970 patients including photos, videos, and ground truth data. Specific statistical face models have been learned using the faces in our database providing an automated parametrization of the facial morphology. The most discriminative morphological features are selected through the importance ranking provided by the random forest algorithm. The random forest approach has also been used to train a classifier on these selected features. We compare a threshold tuning method based on class prior with two methods, which learn an optimal threshold on a training set for tackling the inherent imbalanced nature of the database. Results: Our fully automated method achieves an AUC of 81.0% in a simplified experimental setup, where only easy and difficult patients are considered. A further validation on the entire database has proven that our method is applicable for real-world difficult intubation prediction, with AUC = 77.9%. Conclusion: The system performance is in line with the state-of-the-art medical diagnosis, based on ratings provided by trained anesthesiologists, whose assessment is guided by an extensive set of criteria. Significance: We present the first completely automatic and noninvasive difficult intubation detection system that is suitable for use in clinical settings.