Machine Learning Wound Healing Rate Prediction
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Solution Overview
Problem
Current methods for assessing wound healing are limited by low precision and inability to accurately predict healing rates, leading to inconsistent treatment plans and potential complications.
Innovation Solution
A method and system using machine learning models to automatically segment wound images, determine three-dimensional wound characteristics, and calculate a continuous linear healing rate to predict wound healing rates based on the equation V/P=−D(c)*t+q.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wound assessment methods based on area and perimeter measurements are used, then the assessment process is simple, but the precision of wound healing rate assessment is low
Solution Approach 1:
The patent transitions from two-dimensional wound area and perimeter measurements to three-dimensional wound volume measurements by incorporating depth information. This dimensional expansion enables more accurate wound healing rate assessment by capturing the complete spatial characteristics of the wound, directly resolving the precision limitation of traditional methods.
Solution Approach 2:
The patent introduces trained machine learning models as intermediaries between the wound image data and the healing rate assessment. These models automatically segment wound images, extract three-dimensional characteristics, and calculate healing rates using the equation V/P=-D(c)*t+q, thereby managing the complexity while achieving high precision measurements.
2Measurement precision
If automated machine learning models are used for wound segmentation and measurement, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service automation where trained machine learning models automatically perform wound image segmentation, extract three-dimensional characteristics, and calculate healing rates without requiring manual intervention. The system serves itself by using the collected data to continuously improve model performance, thereby managing complexity through automation while maintaining high precision.
Solution Approach 2:
The patent changes the measurement parameters from simple two-dimensional area and perimeter to comprehensive three-dimensional characteristics including volume, depth, surface area, and perimeter. This parameter expansion, processed through automated machine learning models, achieves superior measurement precision while the automation manages the increased complexity of handling multiple parameters.
3Reliability
If three-dimensional wound characteristics are measured, then a comprehensive view of healing is achieved, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on wound image data before actual assessment. These pre-trained models automatically segment wound images and extract three-dimensional characteristics, thereby simplifying the data processing complexity during actual use while ensuring reliable and comprehensive healing assessment through accurate three-dimensional measurements.
Solution Approach 2:
The patent implements feedback mechanisms where the collected three-dimensional wound data is used to continuously train and improve the machine learning models. This feedback loop enhances the reliability of healing assessment by refining the models' ability to accurately capture and process complex three-dimensional characteristics, while the automation progressively manages the data processing complexity.
Data Source
AI summary
This disclosure provides a novel method and system for wound assessment and treatment to improve wound healing. The disclosed method and system employs machine learning models to extract information from collected wound images and other data to determine wound healing rates and appropriate treatment plans.