Optical Wound Imaging With Machine Learning for Healing Prediction
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Solution Overview
Problem
Existing wound assessment technologies are invasive, costly, and inaccurate in predicting the healing potential of diabetic foot ulcers and other wounds, leading to delayed and ineffective treatment decisions.
Innovation Solution
Non-invasive, point-of-care optical imaging systems using machine learning algorithms to analyze tissue reflectance at multiple wavelengths, enabling rapid classification and prediction of wound healing parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wound assessment methods are used, then treatment decisions can be made, but the assessment is invasive, costly, and inaccurate in predicting healing potential
Solution Approach 1:
The patent replaces traditional mechanical/invasive wound assessment methods (such as biopsy or physical examination) with optical imaging technology. The system uses light to non-invasively capture images of the wound and surrounding tissue, then applies machine learning algorithms to analyze the images and predict healing outcomes. This substitution eliminates the need for invasive procedures while maintaining or improving assessment accuracy.
Solution Approach 2:
The patent transforms the assessment approach by changing from direct physical measurement to optical parameter analysis. The machine learning model analyzes multiple parameters from optical images including color, texture, reflectance, and other visual characteristics to predict healing potential. This parameter transformation enables non-invasive assessment while capturing comprehensive wound information.
2Productivity
If traditional wound assessment methods are used, then treatment can be initiated, but healing times are prolonged and amputations increase
Solution Approach 1:
The patent performs preliminary assessment of healing potential at the initial wound visit using optical imaging and machine learning. By predicting which wounds are likely to heal and which require intervention before significant deterioration occurs, the system enables timely initiation of appropriate treatments. This preliminary action prevents delays that would otherwise occur while waiting for traditional assessment results or for wounds to progress to critical stages.
Solution Approach 2:
The system provides feedback on predicted healing outcomes to guide treatment decisions. By analyzing optical images and providing probabilistic predictions of healing potential, the system creates a feedback loop that enables clinicians to adjust treatments based on predicted responses, thereby optimizing healing rates and reducing time loss.
3Measurement precision
If optical imaging at multiple wavelengths is used, then accurate tissue classification is achieved, but device complexity increases
Solution Approach 1:
The patent employs a single optical imaging device that captures images across multiple wavelengths, making the system multi-functional. Rather than requiring separate devices for different wavelength measurements, the unified system performs comprehensive tissue analysis by capturing spectral information across the visible range, thereby achieving high classification accuracy without proportionally increasing device complexity.
Solution Approach 2:
The system uses standard digital camera sensors to capture optical images, leveraging existing technology rather than requiring specialized detectors for each wavelength. This approach copies the functionality of complex spectral imaging systems using more accessible, less complex components, thereby maintaining measurement precision while reducing overall device complexity.
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
Enables early and accurate determination of wound healing potential, allowing for timely selection of appropriate therapies and reducing healing times and amputations.
Implementation Method 1
at least one light detection element configured to collect light of at least a first wavelength after being reflected from a tissue region comprising a wound
Data Source
AI summary
Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.