Semi-Automated Nasal PAP Mask Sizing using Facial Photographs

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.