Automatic Nasal PAP Mask Sizing with a Deep Unet

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 mod...

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Bibliographic Details
Published inProceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society pp. 6115 - 6118
Main Authors Johnston, Benjamin, de Chazal, Philip
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
facial landmarking
machine learning
OSA
PAP
telemedicine
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ISSN1558-4615
DOI10.1109/EMBC44109.2020.9176291

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Summary: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).
ISSN:1558-4615
DOI:10.1109/EMBC44109.2020.9176291