Causal CT Stroke Imaging for Explainable Onset-Time Estimation
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Solution Overview
Problem
Existing methods for estimating the onset time of acute ischemic stroke and predicting short-term progression lack intuitive visual explanations and incorporate limited clinical intuition, relying on static models that do not provide comprehensive insights.
Innovation Solution
A computer-implemented method using a causal model that integrates computed tomography imaging data to generate counterfactual images, allowing interventions on variables such as onset time, demographic, and therapeutic information, providing intuitive exploration and validation through synthetic images at different time points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If learning-based regression models are used to estimate onset time, then automation and efficiency are improved, but clinical intuition and explainability are lost
Solution Approach 1:
The patent introduces causal models as an intermediary between learning-based regression models and clinical users. These causal models generate counterfactual images that serve as explanatory intermediaries, showing what the stroke appearance would look like under different causal scenarios (e.g., different onset times or treatment interventions), thereby restoring clinical intuition while maintaining automation efficiency
Solution Approach 2:
The patent creates counterfactual copies of the actual stroke images by applying causal interventions to the causal model. These synthetic counterfactual images copy the visual appearance of stroke at different time points or under different conditions, providing intuitive visual explanations without requiring clinicians to understand complex regression model internals
2Loss of information
If static models with quantitative measurements are used, then some measure of explanation is provided, but visual intuition and flexibility are limited
Solution Approach 1:
The patent transforms static quantitative models into dynamic visual models by implementing a causal model that can generate counterfactual images for different time points and intervention scenarios. This allows dynamic exploration of stroke progression and treatment effects through visual imagery rather than fixed numerical outputs
Solution Approach 2:
The patent enables flexible parameter exploration by allowing users to intervene on causal variables (such as onset time, demographic factors, or therapeutic information) and observe how changes in these parameters affect the generated counterfactual images, providing both explanation and visual intuition simultaneously
Data Source
AI summary
One or more example embodiments of the invention relates in one aspect to a computer-implemented method for providing a stroke information. The method includes receiving examination data, the examination data comprising computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising at least one brain region, the at least one brain region being affected by a stroke; adjusting a causal model based on the computed tomography imaging data to obtain an adjusted causal model, wherein the adjusted causal model models a first variable as a first cause for an appearance of the examination area of the patient; receiving a first value for the first variable; generating the stroke information based on the adjusted causal model and the first value for the first variable; and providing the stroke information.