Facial and Neck Image Analysis for Sleep Disorder Risk Scoring
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Solution Overview
Problem
Current methods for diagnosing sleep disorders, such as Obstructive Sleep Apnea (OSA), Cheyne-Stokes Respiration (CSR), Obesity Hyperventilation Syndrome (OHS), and Chronic Obstructive Pulmonary Disease (COPD), are inaccurate due to subjective patient reports and resource-intensive sleep labs, which many patients avoid.
Innovation Solution
A system that analyzes digital images of a patient's face and neck using a mobile device, employing machine learning to identify phenotypes and correlate them with sleep disorders, providing a risk score based on feature measurements and physiological data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sleep labs are used to diagnose sleep disorders, then diagnostic accuracy is improved, but resource consumption and time cost increase
Solution Approach 1:
The patent uses digital images and 3D models as copies of the patient's anatomical structures to perform diagnostic analysis. Instead of requiring patients to undergo time-consuming sleep lab procedures, the system creates virtual representations (2D images, 3D models) of the patient's face and neck, then analyzes these models using machine learning algorithms to diagnose sleep disorders. This copying approach maintains diagnostic accuracy while eliminating the need for extended sleep lab observation periods.
Solution Approach 2:
The system performs preliminary diagnostic assessment using readily available digital images and anatomical measurements before committing patients to full sleep lab procedures. By analyzing phenotypes, facial structures, and neck measurements in advance, the system can identify high-risk patients who would benefit most from sleep lab confirmation, thereby reducing overall time costs while maintaining diagnostic accuracy for those who need comprehensive evaluation.
2Measurement precision
If sleep labs are used to diagnose sleep disorders, then diagnostic accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent replaces resource-intensive sleep lab equipment and facilities with digital image processing and machine learning analysis. By using standard digital cameras and computational algorithms to create and analyze 3D models of patient anatomy, the system achieves comparable diagnostic accuracy without requiring specialized sleep lab infrastructure, staff, and equipment.
Solution Approach 2:
The system enables patients to undergo preliminary diagnostic assessment in the comfort of their own homes using their personal mobile devices. Patients can capture images of their own faces and necks, and the machine learning system automatically performs the diagnostic analysis without requiring sleep lab staff, equipment, or facility resources. This self-service approach dramatically reduces resource consumption while maintaining diagnostic capability.
3Ease of operation
If subjective patient reports are used to diagnose sleep disorders, then ease of operation is improved, but diagnostic accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary machine learning system that objectively analyzes digital images and anatomical measurements to bridge the gap between easy patient participation and accurate diagnosis. Instead of relying solely on subjective patient reports or complex sleep lab procedures, the system uses an automated algorithmic intermediary to process objective visual data and provide accurate diagnostic assessments with minimal patient effort.
Solution Approach 2:
The system replaces the mechanical process of subjective patient reporting with an automated machine learning analysis system. Instead of patients filling out questionnaires or describing their symptoms (subjective mechanical processes), the system uses computer vision algorithms to objectively analyze anatomical features from digital images, thereby improving diagnostic accuracy while maintaining ease of operation.
Data Source
AI summary
A system and method to determine a sleep disorder in a patient is disclosed. A storage device stores a digital image including a face and a neck of the patient. A database stores previously identified phenotypes and dimensions of facial and neck features. A sleep disorder analysis engine is coupled to the storage device and the database. The sleep disorder analysis engine is operable to identify features of the face and the neck from the image by determining landmarks on the image. The sleep disorder analysis engine classifies at least one phenotype from the image based on comparisons with the database. The sleep disorder analysis engine correlates the at least one phenotype and at least one feature with a sleep disorder. The sleep disorder analysis engine determines a risk score of the sleep disorder based on the correlation of the phenotype and the feature.