Automatic Mallampati Classification Using Active Appearance Models

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.

Development and evaluation of double locus sequence typing for molecular epidemiological investigations of Clostridium difficile.

Despite the development of novel typing methods based on whole genome sequencing, most laboratories still rely on classical molecular methods for outbreak investigation or surveillance. Reference methods for Clostridium difficile include ribotyping and pulsed-field gel electrophoresis, which are band-comparing methods often difficult to establish and which require reference strain collections. Here, we present the double locus sequence typing (DLST) scheme as a tool to analyse C. difficile isolates. Using a collection of clinical C. difficile isolates recovered during a 1-year period, we evaluated the performance of DLST and compared the results to multilocus sequence typing (MLST), a sequence-based method that has been used to study the structure of bacterial populations and highlight major clones. DLST had a higher discriminatory power compared to MLST (Simpson's index of diversity of 0.979 versus 0.965) and successfully identified all isolates of the study (100 % typeability). Previous studies showed that the discriminatory power of ribotyping was comparable to that of MLST; thus, DLST might be more discriminatory than ribotyping. DLST is easy to establish and provides several advantages, including absence of DNA extraction [polymerase chain reaction (PCR) is performed on colonies], no specific instrumentation, low cost and unambiguous definition of types. Moreover, the implementation of a DLST typing scheme on an Internet database, such as that previously done for Staphylococcus aureus and Pseudomonas aeruginosa ( http://www.dlst.org ), will allow users to easily obtain the DLST type by submitting directly sequencing files and will avoid problems associated with multiple databases.