Treffer: A machine learning algorithm to predict changes in the upper airway during mouth opening to support the design of a video laryngoscope blade

Anatomical variations in the upper airway significantly impact the effectiveness of video laryngoscope blades. Existing literature on upper airway dynamics and blade design lacks a comprehensive framework to address these variations. The proposed model uses the extent of mouth opening with three demographic features and three anatomical features in the closed-mouth state to predict the anatomical features in the open-mouth state, which can support the design of a laryngoscope blade. Pearson’s correlation was studied to understand the correlation between the features, and the ordinary least square method was used to develop a model. For all three outputs, a separate model was developed, which gave R-squares of 0.98,0.74 and 0.94. The findings highlight the potential of data-driven approaches to optimize laryngoscope blade designs.