Difficult tracheal intubation assessment is an important
research topic in anesthesia as failed intubations are
important causes of mortality in anesthetic practice. The
modified Mallampati score is widely used, alone or in
conjunction with other criteria, to predict the
difficulty of intubation. This work presents an automatic
method to assess the modified Mallampati score from an
image of a patient with the mouth wide open. For this
purpose we propose an active appearance models (AAM)
based method and use linear support vector machines (SVM)
to select a subset of relevant features obtained using
the AAM. This feature selection step proves to be
essential as it improves drastically the performance of
classification, which is obtained using SVM with RBF
kernel and majority voting. We test our method on images
of 100 patients undergoing elective surgery and achieve
97.9% accuracy in the leave-one-out crossvalidation test
and provide a key element to an automatic difficult
intubation assessment system.