Machine Learning Wound Imaging for Future Appearance Prediction

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Solution Overview

Problem

Existing wound treatment methods rely solely on the current state of the wound to guide treatment decisions, lacking the ability to predict future wound appearance and adjust treatment plans accordingly, which can lead to suboptimal outcomes.

Innovation Solution

A prediction system using machine learning models processes a time series of wound images and metadata to generate predicted images of the future wound appearance, enabling early determination of optimal treatment strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If machine learning models are used to predict future wound appearance, then treatment planning accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvetreatment planning accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

A machine learning model acts as an intermediary between wound image data and treatment decisions. The model processes sequential wound images and generates predicted future wound appearances, enabling more accurate treatment planning without requiring complex manual analysis. The model serves as a mediator that transforms raw image data into actionable predictive insights.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates a predictive copy or simulation of future wound appearance based on historical image data. Instead of directly observing the future state, the machine learning model generates a virtual representation of how the wound will evolve, allowing clinicians to evaluate different treatment scenarios without waiting for actual healing progression.

Inventive Principle:

Principle #26Copying

2Measurement precision

If sequential wound images are processed through machine learning models, then prediction accuracy is improved, but processing time increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The machine learning model is pre-trained on extensive wound image datasets before deployment. This preliminary training phase allows the model to learn wound healing patterns and relationships, so that during actual use, it can quickly process new sequential images and generate accurate predictions without requiring extensive computation time for each individual case.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250322936A1Artificial intelligence techniques for generating a predicted future image of a wound

Publication Date: 2025.10.16 SOLVENTUM INTELLECTUAL PROPERTIES CO

AI summary

An example system includes processors configured to: obtain image capture data for a sequence of one or more images representative of an appearance of a wound at a corresponding image capture time, each of the images separated by a sampling time interval between the image and a next image, pass the image capture data for the sequence of images through a machine learning model trained to generate image data representing one or more predicted images of the future appearance of the wound at a corresponding future time wherein a prediction time interval between the future time and a capture time of a last image of the sequence of images is greater than each of the sampling time intervals, and output the image data representing the one or more predicted images of the future appearance of the wound.