Semi-Automated Nasal PAP Mask Sizing using Facial Photographs
Benjamin Johnston, Alistair McEwan, Philip de Chazal

TL;DR
This paper introduces a semi-automated system utilizing neural networks to accurately size nasal PAP masks from facial photographs, offering a promising alternative to manual measurement methods.
Contribution
The study develops a neural network-based system for nasal PAP mask sizing that achieves high accuracy and compares favorably with manual measurement techniques.
Findings
72% accuracy in correct mask sizing
96% accuracy within one size group
Comparable performance to manual measurements
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.
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