Machine-Learned Wound Classification for Healing Progress Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional healthcare systems lack effective methods for coordinating wound treatment between multiple individuals and tracking wound progression over time, leading to gaps in critical information transfer and inadequate wound care due to lack of background knowledge and insufficient tracking capabilities.
Innovation Solution
A patient management system utilizing machine-learned models to analyze wound images, classify wounds, and track their progression over time, providing standardized image capture and treatment recommendations based on machine learning and rules-based algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional healthcare systems use verbal communication and generic medical information sharing systems, then coordination between healthcare providers is maintained, but critical wound information is lost and tracking capabilities are insufficient
Solution Approach 1:
The system creates digital copies of wound images and stores them in a standardized database format, allowing accurate replication and sharing of wound information across multiple healthcare providers without loss of critical details. The machine learning model processes these copied images to extract and preserve essential wound characteristics.
Solution Approach 2:
The patent introduces a specialized wound management system as an intermediary between healthcare providers, patients, and relatives. This intermediary system standardizes information exchange, ensures complete wound data transmission, and provides tracking capabilities that generic systems lack, resolving the information loss problem without requiring complete system restructuring.
2Ease of operation
If bedside nurses and caregivers lack wound care background knowledge, then general healthcare delivery is maintained, but treatment determination and WOC referral timing are compromised
Solution Approach 1:
The system enables bedside nurses and caregivers to independently assess wound conditions and determine appropriate treatments through the machine learning model's automated analysis. The system provides treatment recommendations and WOC referral guidance based on objective image analysis, allowing non-experts to perform wound care functions previously requiring specialized knowledge.
Solution Approach 2:
The patent replaces the need for human expert knowledge (mechanical cognitive process) with an automated machine learning system that analyzes wound images and provides treatment recommendations. This substitution maintains reliability by using consistent, objective criteria while improving ease of operation for non-specialist caregivers.
3Measurement precision
If conventional systems lack wound progression tracking capabilities, then simple monitoring is maintained, but ability to assess healing or deterioration over time is insufficient
Solution Approach 1:
The system establishes a standardized baseline wound image and storage protocol from the beginning of wound care. By pre-defining the imaging standard and creating an initial reference point, the system enables precise progression measurement without requiring complex retrospective adjustments or re-calibration.
Solution Approach 2:
The patent segments the wound assessment process into distinct components: image capture, machine learning analysis, characteristic extraction, and progression comparison. This segmentation allows precise measurement of specific wound parameters while keeping the overall system manageable through modular processing steps.
4Measurement precision
If standardized image capture protocols are implemented, then wound classification accuracy is improved, but ease of image capture may be reduced
Solution Approach 1:
The system uses machine learning to automatically adjust image processing parameters based on the captured image quality. Rather than requiring perfect manual capture conditions, the system compensates for variations in lighting, angle, and focus through automated parameter adjustment, maintaining accuracy while preserving ease of capture.
Solution Approach 2:
The machine learning model provides real-time feedback on image quality and captures adequacy. The system analyzes the captured image and guides the user on whether the image meets classification requirements or needs to be retaken, simplifying the process by providing clear pass/fail criteria and improvement guidance.
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.