CDFAM Computational Design Symposium — Barcelona 2026
Anne Pasman, Emmy Kerssen · Saxion Hogeschool
CPAP therapy is used to treat patients with sleep apnea by providing constant air pressure through a mask during sleep. For many patients (75,4%), standard masks fit poorly or require tight adjustment, causing issues such as pressure points or leakage resulting in dry eyes, skin irritation, and finally even therapy discontinuation (30%). Personalized CPAP masks can improve fit and comfort and reduce leakage.
However, early designs face challenges due to the interaction between soft facial tissue and mask materials. During sleep, facial deformation and mask compliance vary with posture, often leading to leakage. In this study, we analyzed 3D scans of individuals lying in multiple positions to model how posture affects mask shape. To improve further, a sensor instrument was developed to measure pressure points while wearing existing masks, this data was used to optimize a computational model that integrates head posture, 3D scan data, and facial softness to guide mask personalization.
Two mask concepts were developed: (1) a fully personalized mask with a 3D-printed soft interface and a rigid hard part available in three sizes, and (2) an intermediate personalized layer that fits between a standard mask and the face to improve fit without replacing the entire mask. Both designs were optimized for 3D printing with Lynxter silicone -improving slicing, print time, neatness, and thickness distribution – as well as for manufacturing with SLA cocoon molding and silicone casting.
Clinical testing in a dutch hospital is planned for March to evaluate the fit of both designs. Results are expected to provide insights into personalized mask design, the feasibility of a 3D scan–model–print workflow in a regional hospital, and the potential of 3D-printed masks for tailored CPAP therapy. When this strategy for generating the model and manufacturing proves potential, the strategy can be extended to other respiratory masks.
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