AI Wound Grading From Images for Pressure Injury Staging
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Solution Overview
Problem
Current wound grading methods in clinical settings are time-consuming and prone to errors due to subjective opinions, leading to delayed and improper treatment, especially for complex wounds like diabetic foot ulcers, which can result in serious complications and increased amputation risk.
Innovation Solution
A method involving a wound image database, semantic segmentation, convolutional neural networks, and deep-learning classifiers to establish a wound grade assessment model that accurately assesses pressure injury stages and wound bed preparation indicators using image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wound grading is performed by plastic surgeons in clinical settings, then diagnostic accuracy can be maintained, but the process is time-consuming and leads to delayed treatment
Solution Approach 1:
The patent creates a digital copy of the wound assessment process through an AI-based image analysis system. The system captures wound images and automatically generates grading results that replicate the expertise of plastic surgeons, enabling rapid assessment without requiring physical presence of specialists for each evaluation.
Solution Approach 2:
The patent replaces the mechanical process of manual visual inspection and subjective judgment by plastic surgeons with an automated AI-based image recognition system. The system uses deep learning models to automatically detect wound characteristics, stage classification, and healing indicators, eliminating the need for continuous human expert intervention in the assessment process.
2Adaptability or versatility
If multiple plastic surgeons are involved in wound assessment, then comprehensive evaluation can be achieved, but subjective opinions lead to diagnostic errors
Solution Approach 1:
The patent ensures homogeneous and consistent wound assessment by using a standardized AI algorithm that applies the same evaluation criteria to all wounds. The deep learning model processes images through identical computational steps, eliminating the variability introduced by different surgeons' subjective interpretations and ensuring uniform grading standards across all cases.
Solution Approach 2:
The system creates a reproducible digital model of wound assessment that can be consistently applied to multiple cases. The AI model captures and replicates the essential features of wound grading in a standardized manner, allowing the same assessment logic to be applied uniformly across different wounds and time points without variation.
3Ease of operation
If manual wound assessment is performed, then clinical judgment can be applied, but treatment planning is delayed
Solution Approach 1:
The system performs preliminary automated assessment and grading of wound images before treatment planning is needed. By pre-processing the image analysis and generating initial grading results automatically, the system prepares essential diagnostic information in advance, allowing treatment planners to focus on developing interventions without waiting for manual assessment completion.
Solution Approach 2:
The patent replaces the manual clinical judgment process with an automated AI system that rapidly analyzes wound images and generates grading results. This substitution maintains the essential diagnostic capabilities while dramatically increasing the speed at which assessment information becomes available for treatment planning.
Data Source
AI summary
A wound care assessment system includes an image capturing device and a processor. The image capturing device is for capturing a target wound image data of a subject. The processor is signally connected to the image capturing device and includes an image-labeling module, a pre-processing module and a wound grade assessment model. The image-labeling module is for receiving and analyzing the target wound image data output from the image capturing device. The pre-processing module is signally connected to the image-labeling module. The wound grade assessment model is established by an establishing method of wound grade assessment model, and the wound grade assessment model is signally connected to the pre-processing module. The wound grade assessment model is for assessing a pressure injury stage and a wound bed preparation indicator of a wound of the subject corresponding to the target wound image data.