Multispectral Wound And Burn Imaging for AI-ML Healing Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wound and burn assessments rely heavily on visual clinical judgment, which is inaccurate, and existing AI/ML models fail to adequately consider tissue blood flow and oxygen information, limiting their effectiveness in wound healing prediction.
Innovation Solution
A multispectral imaging system using multiple wavelength bands and synchronized imaging channels, combined with AI-ML analysis, to provide detailed tissue physiology information and enhance wound assessment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual clinical judgment is used for wound assessment, then the method is simple and quick, but the accuracy is low (only 70% for trained specialists, 50% for front line physicians)
Solution Approach 1:
The imaging system divides the wound assessment into multiple spectral bands (visible light, ultraviolet, near infrared) and multiple imaging channels (anatomical structure, functional characteristics, compositional characteristics). Each band and channel provides specific information about different tissue properties, allowing comprehensive assessment beyond single-modality limitations.
Solution Approach 2:
The system transitions from two-dimensional visible color images to multi-dimensional spectral data by adding ultraviolet and near infrared bands. This dimensional expansion enables detection of tissue properties (blood flow, oxygenation, moisture) that are invisible to the human eye but critical for accurate wound assessment.
2Measurement precision
If existing AI/ML models are used for wound healing prediction, then processing is automated, but they fail to consider tissue blood flow and oxygen information
Solution Approach 1:
The imaging system is designed to simultaneously capture multiple types of tissue information across different spectral bands and imaging channels. The same multispectral imaging platform provides anatomical structure visualization, functional characteristics (blood flow, oxygenation), and compositional characteristics (moisture, pH) in a single integrated system, ensuring no critical information is lost.
Solution Approach 2:
The system introduces an intermediary multispectral imaging layer between the wound tissue and the AI/ML analysis. This imaging layer captures and processes physical optical signals into multiple data channels, which then serve as enriched input features for the machine learning models, enabling them to detect tissue physiology information that would otherwise be inaccessible.
3Loss of information
If multispectral imaging with multiple wavelength bands is used, then tissue physiology information is captured, but the device complexity and hardware requirements increase
Solution Approach 1:
The system merges multiple imaging modalities (visible light imaging, ultraviolet imaging, near infrared imaging) into a single integrated multispectral imaging platform. By combining these modalities spatially and temporally synchronized, the system captures comprehensive tissue information while sharing common hardware components (light sources, detectors, processing units) to manage complexity.
Solution Approach 2:
The imaging system performs continuous multispectral capture across multiple wavelength bands in a synchronized manner, maintaining continuous acquisition of anatomical, functional, and compositional information. This continuous multi-band imaging ensures complete data collection without requiring separate sequential measurements, improving efficiency despite the complexity of multiple sensors.
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
Improves wound assessment accuracy by integrating multispectral imaging and AI-ML, enabling precise tissue analysis and predictive wound healing insights.
Implementation Method 1
Multispectral technologies allow combining light of visible light, ultraviolet (UV) and near infrared (NIR) wavelengths during the imaging process and provide benefits of visualizing anatomical structure and quantitively visualizing distribution of functional, physiologic and compositional characteristics of organs and tissues
Implementation Method 2
The imaging hardware can include a light source of multiple bands of wavelength of noncoherent and coherent light for broadband, narrowband, fluorescence, autofluorescence, Laser Speckle Imaging (LSI), Laser Doppler Imaging (LDI)
Implementation Method 3
Laser Doppler Imaging (LDI), tissue oxygenation imaging
Implementation Method 4
The imaging hardware can include a light source of multiple bands of wavelength of noncoherent and coherent light for broadband, narrowband, fluorescence, autofluorescence
Implementation Method 5
capturing multiple channels of images in a temporally and spatially synchronized fashion for image processing, analysis, visualization, and reporting
Data Source
AI summary
Methodology of various imaging modalities and engineering features are provided for multispectral soft tissue imaging architecture. The architectural designs comprise hardware of multiple ranges of wavelength for illumination and camera sensing and software for image acquisition, processing, feature abstraction, artificial intelligence-machine learning (AI-ML) analysis, visualization, and reporting. Embodiments of imaging hardware in a medical device can include a light source of multiple bands of wavelength of noncoherent and coherent light for broadband, narrowband, fluorescence, autofluorescence, Laser Speckle Imaging (LSI), Laser Doppler Imaging (LDI), tissue oxygenation imaging, and other variation of soft tissue imaging modalities. The imaging software can include temporally and spatially synchronized acquisition of multiple imaging channels, image processing based on physics principle and mathematical equations of each imaging modality, feature engineering to abstract key parameters, AI-ML training and predictive analysis, image fusion-based visualization and report. The system is designed to be either an addon to smart phones, tablets or independent handheld equipment based on embedded System On Module (SOM).