Abstract
We present the use of a deep Unet convolutional neural network as an automated way of sizing nasal Positive Airway Pressure (PAP) masks using facial images of patients. Using a VGG16 backbone the network was trained with the MUCT dataset and a significant amount of data augmentation. The trained model was then applied to a small custom dataset of PAP and non-PAP patients to predict the nose widths and corresponding PAP mask sizes of each subject. The Unet model produced a mask sizing accuracy of 63.73% (116/183) and a within one size accuracy of 88.5% (162/183).
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