Multimodal Medical Condition Detection Without Sleep Lab Equipment
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Solution Overview
Problem
Traditional diagnostic methods for medical conditions like obstructive sleep apnea are costly, equipment-intensive, and often inaccurate due to limited parameter capture and subjective interpretations, particularly affecting underserved demographics.
Innovation Solution
A system utilizing multi-modal data, including imagery, voice samples, and questionnaires, processed by machine learning models to predict medical conditions, reducing the need for expensive equipment and improving diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used, then diagnostic accuracy can be maintained through comprehensive parameter capture, but equipment cost and operational expense increase significantly
Solution Approach 1:
The patent extracts and focuses on specific high-value diagnostic parameters (craniofacial features, voice characteristics, questionnaire data) rather than capturing all possible physiological parameters. This selective extraction enables accurate sleep apnea diagnosis using only a smartphone camera, microphone, and software algorithms, eliminating the need for expensive polysomnography equipment while maintaining diagnostic effectiveness for the target condition
Solution Approach 2:
The system creates a digital model (3D reconstruction) of the patient's craniofacial anatomy from 2D images captured by a smartphone camera. This virtual copy replaces the need for physical 3D scanning equipment or MRI/CT scanners, achieving accurate anatomical measurement and analysis through software-based reconstruction from accessible imaging sources
2Reliability
If traditional diagnostic equipment is deployed, then comprehensive medical data can be collected, but accessibility and ease of administration deteriorate
Solution Approach 1:
The smartphone serves as a universal diagnostic platform that combines camera, microphone, display, and processing capabilities into a single device already possessed by most patients. This multi-functional device replaces multiple specialized medical instruments (3D scanner, audio recorder, questionnaire system), enabling comprehensive data collection through a single accessible tool that patients can operate independently at home
Solution Approach 2:
The system enables patients to perform their own diagnostic evaluation by capturing images, recording voice samples, and completing questionnaires using their personal smartphones without requiring medical professional intervention. The automated processing algorithms analyze the collected data and generate diagnostic assessments, allowing patients to initiate and complete the diagnostic process independently, dramatically improving accessibility for underserved populations
3Measurement precision
If multiple diagnostic parameters are measured, then diagnostic accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The diagnostic system segments the analysis into distinct modular components: craniofacial geometry extraction from images, voice signal processing for respiratory sound analysis, questionnaire response evaluation, and integration of results. Each module processes specific data types independently using specialized algorithms, then combines results to form the comprehensive diagnosis. This segmentation reduces processing complexity compared to attempting to analyze all parameters simultaneously in a single unified system
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for medical condition identification. One of the methods includes obtaining visual data representing at least one body part of a subject; obtaining non-visual data that corresponds to one or more biological characteristics of the subject; providing data representing (i) the visual data and (ii) the non-visual data to one or more machine learning models, wherein the one or more machine learning models are trained to predict presence of a medical condition; obtaining an output of the one or more machine learning models that is generated based on the one or more machine learning models processing the visual data and the non-visual data; and determining presence of the medical condition for the subject using the output of the one or more machine learning models.