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Development of a smart-fit system for CPAP interface selection

Lookup NU author(s): Zhichao Ma, Dr Philip Hyde, Dr Javier Munguia ValenzuelaORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Continuous Positive Airway Pressure (CPAP) therapy is commonly prescribed for longstanding, acute cases of Obstructive Sleep Apnea (OSA) during which patients must wear a tight-fitting breathing mask overnight for the duration of the treatment. Because this condition frequently leads to the permanent use of CPAP masks, interface selection is a crucial factor influencing the treatment quality and effectiveness. Masks/interface selection is normally performed on a trial an error basis with clinicians informing their selection based on OSA-related factors with basic fitting feedback from patients. However, it is not uncommon for patients to abandon the treatment or request additional consultations due to ill-fitting CPAP mask with the main sources of discomfort being perceived air leakage and mask/strap overtightening leading to skin damage. This work introduces a novel system (Smart-Fit), for CPAP interface selection using advanced digital technologies, such as Reverse Engineering and Computational Modeling (Finite Element Analysis) which are paired to evaluate and determine the best fitting interface for each clinical case. The model simplifies the number of 3D facial landmarks to 12 and established that a 2 mm scan resolution is enough for accurate scans. The Von Mises stress map in ANSYS serves as an indicator of potential high-pressure areas, triggering the need for a chance of mask size. Current results indicate the Smart Fit System can enable a ‘‘best fit CPAP interface’’ to be selected considering individual’s physical characteristics and existing CPAP interface configurations. The development of the Smart Fit System is an evolution compared to traditional CPAP interface selection approach, which optimizes the CPAP interface selection process.

Publication metadata

Author(s): Zhichao M, Hyde P, Drinnan M, Munguia J

Publication type: Article

Publication status: Published

Journal: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine

Year: 2021

Volume: 235

Issue: 1

Pages: 44-53

Print publication date: 01/01/2021

Online publication date: 28/09/2020

Acceptance date: 27/08/2020

Date deposited: 17/11/2020

ISSN (print): 0954-4119

ISSN (electronic): 2041-3033

Publisher: Sage Publications Ltd


DOI: 10.1177/0954411920959879


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