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Identification of Difficult to Intubate Patients from Frontal Face Images Using an Ensemble of Deep Learning Models by Thomas E. Tavolara; Metin N. Gurcan; Scott Segal; M.K.K. Niazi is a Medicine article available to read on EtoBox.

What is Identification of Difficult to Intubate Patients from Frontal Face Images Using an Ensemble of Deep Learning Models about?

Failure to identify difficult intubation is the leading cause of anesthesia-related death and morbidity. Despite preoperative airway assessment, 75-93% of difficult intubations are unanticipated, and airway examination methods underperform, with sensitivities of 20-62% and specificities of 82-97%. To overcome these impediments, we aim to develop a deep learning model to identify difficult to intubate patients using frontal face images. We proposed an ensemble of convolutional neural networks which leverages a database of celebrity facial images to learn robust features of multiple face regions. This ensemble extracts features from patient images (n = 152) which are subsequently classified by a respective ensemble of attention-based multiple instance learning models. Through majority voting, a patient is classified as difficult or easy to intubate. Whereas two conventional bedside tests resulted in AUCs of 0.6042 and 0.4661, the proposed method resulted in an AUC of 0.7105 using a cohort of 76 difficult and 76 easy to intubate patients. Generic features yielded AUCs of 0.4654-0.6278. The proposed model can operate at high sensitivity and low specificity (0.9079 and 0.4474) or low se

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Author

Thomas E. Tavolara; Metin N. Gurcan; Scott Segal; M.K.K. Niazi

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Medicine (Physical Sciences)