Abstract-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-ofthe-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 non-invasive difficult intubation detection system that is suitable for use in clinical settings.
Abstract-Driving requires the constant coordination of many body systems and full attention of the person. Cognitive distraction (subsidiary mental load) of the driver is an important factor that decreases attention and responsiveness, which may result in human error and accidents. In this paper, we present a study of facial expressions of such mental diversion of attention. First, we introduce a multi-camera database of 46 people recorded while driving a simulator in two conditions, baseline and induced cognitive load using a secondary task. Then, we present an automatic system to differentiate between the two conditions, where we use features extracted from Facial Action Unit (AU) values and their cross-correlations in order to exploit recurring synchronization and causality patterns. Both the recording and detection system are suitable for integration in a vehicle and a real-world application, e.g. an early warning system. We show that when the system is trained individually on each subject we achieve a mean accuracy and F-score of ∼ 95%, and for the subject independent tests ∼ 68% accuracy and ∼ 66% F-score, with person-specific normalization to handle subject dependency. Based on the results, we discuss the universality of the facial expressions of such states and possible real-world uses of the system.
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