3D Facial Modeling for Automated Sleep Mask Selection
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Solution Overview
Problem
Current mask selection for sleep apnea treatment is largely an art rather than a science, leading to inconsistent and inefficient methods, with clinicians relying on trial and error and limited sizing tools, resulting in suboptimal mask fitting and increased resource expenditure.
Innovation Solution
A system utilizing a visual presentation and interaction component, such as a tablet PC or smartphone, captures images and patient responses to determine the most suitable mask through a scientific method, including 3D modeling and elimination of less suitable options, providing a personalized mask recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If trial and error method is used for mask selection, then clinician experience and judgment are utilized, but mask fitting consistency and precision deteriorate
Solution Approach 1:
The patent replaces the mechanical trial-and-error fitting process with an automated image processing system. The system captures multiple images of the patient's face, processes them through algorithms to create a 3D facial model, and automatically determines the optimal mask size and type, eliminating the need for manual trial and error by clinicians.
Solution Approach 2:
The patent creates a digital copy (3D facial model) of the patient's face from captured images. This virtual model serves as a precise replica that can be measured and analyzed without physical contact, allowing the system to determine mask specifications based on accurate geometric data rather than subjective clinical judgment.
2Device complexity
If limited sizing tools are used, then device complexity is reduced, but mask selection accuracy and consistency deteriorate
Solution Approach 1:
The patent transitions from traditional 2D sizing tools to a 3D facial modeling system. By capturing multiple images and processing them into a three-dimensional representation of the patient's face, the system gains the additional dimension of depth and spatial relationship, enabling precise measurement of complex facial geometries that 2D tools cannot accommodate.
Solution Approach 2:
The system performs preliminary image capture and processing to create a 3D facial model before the actual mask selection process. This preliminary action establishes an accurate geometric foundation that guides all subsequent mask sizing decisions, ensuring precision is built into the foundation rather than added during measurement.
3Quantity of substance
If manual mask selection process is used, then resource expenditure is reduced, but time required for mask fitting and patient compliance deteriorate
Solution Approach 1:
The system enables self-service mask selection by automatically analyzing the patient's facial geometry and determining the optimal mask specifications without requiring extensive clinician involvement. The automated algorithm processes images and generates mask recommendations independently, reducing the time and resources that would otherwise be spent on manual measurement and trial fitting.
Solution Approach 2:
The system incorporates feedback loops where the captured images are processed to create a 3D model, which then provides feedback information about the patient's facial geometry that guides mask selection. This automated feedback mechanism eliminates the need for repeated manual measurements and adjustments, significantly reducing the time required to reach a satisfactory mask fit.
Data Source
AI summary
A method of identifying a particular mask for a patient for use in delivering a flow of breathing gas to the patient includes capturing with a visual presentation and interaction component a plurality of images of the patient; receiving with the visual presentation and interaction component a number of responses from the patient to questions presented to the patient, eliminating one or more masks from a pool of potential masks for the patent based on at least one of the responses, utilizing at least some images of the plurality of images to determine the particular mask for the patient from the pool of potential masks, and identifying the particular mask to the patient.