Respiratory Mask Fit Selection Using Facial Imaging and Patient Feedback
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Solution Overview
Problem
Existing respiratory pressure therapy masks face challenges such as discomfort, poor fit, and difficulty in selecting the appropriate size, leading to reduced patient compliance due to labor-intensive sizing processes and lack of user feedback integration in design improvements.
Innovation Solution
A system that collects facial image data, operational data, and subjective feedback from a population of patients to correlate and adjust mask characteristics using machine learning, facilitating personalized mask design and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual sizing processes are used for respiratory masks, then manufacturing precision can be maintained, but labor intensity increases and patient compliance decreases
Solution Approach 1:
The patent replaces manual mechanical measurement processes with an automated image processing system. The system captures facial images and automatically extracts dimensional data through computer vision algorithms, eliminating the need for manual sizing procedures while maintaining precision in mask fit determination.
Solution Approach 2:
The patent creates a digital copy of the patient's facial geometry through image capture and processing. This digital model serves as a template for selecting appropriate mask sizes, replacing the need for physical manual measurements and enabling rapid, repeatable sizing without labor-intensive processes.
2Ease of manufacture
If standard mask designs are used, then manufacturing simplicity is maintained, but adaptability to individual patient facial features decreases
Solution Approach 1:
The patent introduces dynamic adaptability into mask selection by using adjustable sizing parameters derived from individual patient facial measurements. Instead of fixed standard sizes, the system determines optimal mask dimensions based on each patient's unique facial geometry, allowing the mask design to adapt dynamically to individual needs while maintaining manufacturing simplicity through parameter-based adjustment.
Solution Approach 2:
The patent changes the parameters of mask selection based on extracted facial dimensional data. By adjusting mask size parameters (length, width, depth) according to measured facial features, the system enables adaptable mask fitting without requiring complex custom manufacturing processes, thus maintaining ease of manufacture while improving adaptability.
3Device complexity
If user feedback is not collected, then device complexity is reduced, but design improvement capability decreases
Solution Approach 1:
The patent implements a feedback mechanism where patient feedback data is collected and integrated into the mask selection process. The system captures user responses regarding mask comfort and fit, processes this information through image analysis, and uses the feedback to refine future mask designs and selections, thereby improving design effectiveness while keeping the system relatively simple.
Data Source
AI summary
A system and method to collect feedback data from a patient wearing an interface such as a mask when using a respiratory pressure therapy device such as a CPAP device. The system includes a storage device including a facial image of the patient. An interface in communication with the respiratory pressure therapy device collect operational data from when the patient uses the interface. A patient interface collects subjective patient input data from the patient in relation to the patient interface. An analysis module correlates a characteristic of the interface with the facial image data, operational data and subjective patient input data.