English

Semi-Automated Nasal PAP Mask Sizing using Facial Photographs

Computer Vision and Pattern Recognition 2017-10-19 v1

Abstract

We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The semi-automated system performed comparably to sizing from manual measurements taken from the same images which produced 89% and 100% accuracy respectively.

Keywords

Cite

@article{arxiv.1709.07166,
  title  = {Semi-Automated Nasal PAP Mask Sizing using Facial Photographs},
  author = {Benjamin Johnston and Alistair McEwan and Philip de Chazal},
  journal= {arXiv preprint arXiv:1709.07166},
  year   = {2017}
}

Comments

4 pages, 3 figures, 4 tables, IEEE Engineering Medicine and Biology Conference 2017