Wound Image Classification for Tracking Healing Progression
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Solution Overview
Problem
Conventional healthcare systems lack effective methods for coordinating wound care among multiple individuals and tracking wound progression over time, leading to gaps in critical information transfer and inadequate treatment decisions.
Innovation Solution
A patient management system utilizing machine-learned models to analyze wound images, classify wounds, and track their progression, providing standardized image capture and data sharing across devices to facilitate informed wound care decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional healthcare systems use verbal communication and generic computing systems for wound care coordination, then the system complexity remains low, but critical information transfer about wound progression is lost
Solution Approach 1:
The patent introduces an intermediary wound care system that includes standardized image capture devices, machine learning classification models, and dedicated communication protocols. This intermediary layer facilitates accurate information transfer between patients, caregivers, and healthcare providers by automatically capturing, classifying, and sharing wound progression data, thereby resolving the information loss problem without requiring complete system redesign.
Solution Approach 2:
The system creates standardized copies of wound images and associated data that can be consistently captured, stored, and transmitted across different devices and platforms. These standardized copies ensure that critical wound information is preserved and transferred accurately without being lost in verbal communication or generic system interfaces.
2Ease of operation
If bedside nurses, patients, and relatives care for wounds without background knowledge, then ease of operation is improved, but treatment decision quality deteriorates
Solution Approach 1:
The machine learning classification model provides automated wound assessment and classification that serves itself by analyzing captured images and generating treatment recommendations without requiring user expertise. This self-service capability allows bedside nurses, patients, and relatives to perform wound care with ease while maintaining reliable treatment decisions through AI-driven analysis.
Solution Approach 2:
The system implements continuous feedback loops where wound images are captured, classified by the machine learning model, and treatment recommendations are provided. This feedback mechanism enables users without background knowledge to make informed treatment decisions by receiving real-time guidance based on objective wound assessment data.
3Measurement precision
If generic medical information sharing systems are used, then device complexity remains low, but wound progression tracking capability is insufficient
Solution Approach 1:
The system transforms wound progression tracking from qualitative generic medical information to quantitative parameters through standardized image capture and machine learning classification. By changing the measurement approach to include standardized imaging parameters and automated analysis, the system achieves precise wound progression measurement while managing complexity through specialized algorithms.
Solution Approach 2:
The patent segments the wound care system into distinct functional components: standardized image capture devices, machine learning classification models, data storage systems, and user interfaces. This segmentation allows each component to be optimized independently, achieving precise wound progression measurement without overwhelming complexity in the overall system.
Data Source
AI summary
This disclosure is directed towards a patient management system for analyzing images of wounds and tracking the progression of wounds over time. In some examples, a computing device of the patient management system receives an image, and determines that the image depicts a wound. The computing device inputs the image into a machine-learned model trained to classify wounds, and receives, from the machine-learned model, a classification of the wound. The computing device may then display the classification of the wound in a user interface. Additionally, the patient management system may train a machine-learned model to classify wounds.