Wound Image ML Triage for Treatment Resource Prediction
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Solution Overview
Problem
Predicting wound management treatment resources is challenging due to the variability in wounds and patients, leading to incorrect resource allocation, which can be harmful to patients and wasteful, and existing manual predictions are prone to human error.
Innovation Solution
Utilizing a trained machine learning model to analyze wound images and patient data to predict necessary treatment resources and facilities, incorporating computer vision and machine learning techniques to accurately determine staffing, equipment, and facility requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prediction methods are used to estimate treatment resources, then the process is simple and quick, but the accuracy is low and prone to human error
Solution Approach 1:
The patent replaces manual mechanical prediction processes with an automated machine learning system. The ML model processes wound images and patient data to generate resource predictions, substituting human judgment with algorithmic analysis. This substitution significantly improves prediction accuracy while the automated nature of the system actually reduces operational complexity despite the sophisticated technology involved.
2Reliability
If highly resourced treatment facilities are recommended for all patients, then patient safety is improved, but resource waste increases and patient comfort decreases
Solution Approach 1:
The patent changes the key parameter from uniform resource allocation to customized resource allocation based on predicted wound characteristics. The ML model generates specific predictions about treatment duration, staffing needs, and facility requirements for each patient, enabling matching patients to appropriate care settings. This parameter change ensures adequate resources for complex cases while avoiding unnecessary resource consumption for simpler cases, thereby reducing waste without compromising safety.
3Productivity
If accurate wound assessment is performed manually, then treatment planning can be customized, but the time and expertise required increase significantly
Solution Approach 1:
The patent replaces manual wound assessment with automated computer vision technology. The ML model analyzes wound images to detect characteristics such as size, depth, and tissue type, performing the assessment rapidly and consistently. This substitution dramatically increases assessment speed while maintaining or improving detection accuracy compared to manual methods, as the algorithm can identify subtle features that may be missed by human observers.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for predicting wound management treatment resources. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first machine learning (ML) model trained to detect wound characteristics from a captured image. The techniques further include predicting at least one of: (i) treatment resources or (ii) a treatment facility for treating the wound, including providing to a second trained ML model characteristics of the wound, patient medical data for the patient, and treatment facility data describing a plurality of available treatment facilities.