Predicting difficult laryngoscopy using ultrasound measurement of distance from skin to epiglottis.

BACKGROUND: Unpredicted difficult intubation can have severe consequences, and it is a significant source of morbidity and mortality. Although recent studies indicate that specific ultrasonography (US) measurements may be predictors of difficult laryngoscopy, their use is still limited, and its quantification is missing. The purpose of this prospective observational study is to evaluate the use of US-measured distance from skin to epiglottis (DSE) for difficult laryngoscopy prediction. METHODS: In a double-blind study, standard preintubation, screening tests, and DSE were obtained from 74 adult patients requiring endotracheal intubation. The relationship between difficult laryngoscopy and DSE was evaluated using a t test. A comparative analysis of its predictive performance with common clinical preintubation screening tests was performed using bootstrapping. RESULTS: We found that increasing DSE is strongly associated with difficult laryngoscopy (P < .001, 2-sided t test). We showed that a cutoff value of 27.5 mm was able to predict difficult laryngoscopy with an accuracy of 74.3%, a sensitivity of 64.7%, and a specificity of 77.1%. CONCLUSIONS: Our work demonstrates that the DSE can be effectively used to predict difficult laryngoscopy. Moreover, combining DSE with the modified Mallampati score in a decision tree significantly improves the predictive power over either test alone.

Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test.

In the Turing test a computer model is deemed to "think intelligently" if it can generate answers that are indistinguishable from those of a human. We developed an analogous Turing-like handshake test to determine if a machine can produce similarly indistinguishable movements. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party - artificial or human with varying levels of noise. The interrogator is asked which party seems to be more human. Here, we compare the human-likeness levels of three different models for handshake: (1) Tit-for-Tat model, (2) λ model, and (3) Machine Learning model. The Tit-for-Tat and the Machine Learning models generated handshakes that were perceived as the most human-like among the three models that were tested. Combining the best aspects of each of the three models into a single robotic handshake algorithm might allow us to advance our understanding of the way the nervous system controls sensorimotor interactions and further improve the human-likeness of robotic handshakes.