Thermal Imaging Sleep Apnea Screening With Multi-Angle AI Analysis
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Solution Overview
Problem
Existing sleep apnea diagnostic methods, particularly thermal imaging, lack efficiency and accuracy, and require hospital visits, which are inconvenient and expose patients to clinical environments.
Innovation Solution
A system using multiple thermal imaging cameras and AI models for capturing and analyzing thermal images from different angles, enabling home-based diagnosis by classifying features into typical or atypical for sleep apnea, providing real-time results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional polysomnography is used for sleep apnea diagnosis, then diagnostic accuracy is improved, but patient convenience deteriorates due to required hospital visits and complex procedures
Solution Approach 1:
The patent uses thermal imaging cameras to capture thermal copies of the patient's face, chest, and abdomen regions during sleep. These thermal images serve as diagnostic data that can be analyzed without requiring the patient to be in a clinical setting, thus maintaining diagnostic capability while improving convenience
Solution Approach 2:
The patent replaces the mechanical and invasive sensors of traditional polysomnography with non-contact thermal imaging technology. Thermal cameras detect heat patterns and temperature changes associated with breathing, eliminating the need for physical sensors attached to the patient's body
2Ease of operation
If home sleep apnea tests are used, then patient convenience is improved, but diagnostic accuracy deteriorates compared to clinical polysomnography
Solution Approach 1:
The patent introduces an AI-based image analysis system as an intermediary between the thermal images and diagnostic conclusions. The AI model processes thermal images to extract breathing patterns, respiratory events, and physiological parameters, bridging the gap between simple home monitoring and accurate clinical diagnosis
Solution Approach 2:
The patent analyzes multiple thermal image parameters including temperature distributions, thermal pattern changes over time, and thermal variations across different body regions. By monitoring these thermal parameters during sleep, the system can detect breathing abnormalities and diagnose sleep apnea with high accuracy
3Ease of operation
If thermal imaging is used for sleep apnea diagnosis, then patient comfort is improved, but diagnostic efficiency deteriorates due to limited accuracy and lack of standardized protocols
Solution Approach 1:
The patent implements pre-trained AI models that have been trained on large datasets of thermal images with known diagnoses. These pre-trained models can immediately analyze new thermal images without requiring manual calibration or setup, enabling efficient and consistent diagnostic performance from the first use
Solution Approach 2:
The system provides real-time feedback by analyzing thermal images continuously during sleep and generating diagnostic results. The AI model processes thermal data streams and provides ongoing assessment of breathing patterns, allowing for immediate detection and diagnosis of sleep apnea events
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system allows for safe, convenient, and efficient home-based diagnosis of sleep apnea with accurate results, reducing the need for hospital visits and enhancing patient comfort.
Implementation Method 1
one or more thermal imaging cameras configured to capture thermal images of the subject during sleep
Data Source
AI summary
A system and method for diagnosing sleep apnea in a subject using one or more thermal imaging cameras and a trained artificial intelligence model. Thermal images of at least a subject's facial, chest, and abdominal regions during sleep are captured for predefined duration and pre-defined intervals. The captured images are processed using an image recognition module to extract features, which are then classified using a trained artificial intelligence model to determine the likelihood of sleep apnea. The system is designed for both residential and clinical settings, enabling efficient, contactless, and user-friendly screening. The results can be presented via an application interface accessible on user devices.