Semi-Automated Nasal PAP Mask Sizing using Facial Photographs
This addresses mask fitting issues for sleep apnea patients, but it is incremental as it builds on existing measurement techniques.
The researchers tackled the problem of sizing nasal Positive Airway Pressure (PAP) masks by developing a semi-automated system using a neural network trained on facial photographs, achieving 72% accuracy for exact sizing and 96% accuracy within one size group.
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.