Prognosis Data Record for Ablation Zone Simulation
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Solution Overview
Problem
Existing medical imaging methods for simulating ablation zones during procedures are inaccurate due to assumptions based on preoperative data, which can lead to suboptimal treatment outcomes.
Innovation Solution
A computer-implemented method that uses a trained function, such as a neural network, to create a prognosis data record by processing first and second image data records, along with operating parameters and positioning information of medical objects like ablation needles, to provide precise predictions of tissue changes during medical interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preoperatively recorded three-dimensional imaging data and assumptions about tissue parameters are used to simulate an ablation zone, then the simulation can be performed, but the accuracy of the simulated ablation zone deteriorates
Solution Approach 1:
The system performs preliminary actions by acquiring multiple types of preoperative imaging data (CT, MRI, ultrasound) and using machine learning models to predict tissue properties before the ablation procedure. This preliminary characterization of tissue parameters improves the accuracy of subsequent ablation zone simulations while maintaining efficient preoperative planning
Solution Approach 2:
The system incorporates feedback mechanisms by comparing predicted ablation zones with actual intraoperative measurements and using this information to refine tissue parameter estimates. The machine learning models are trained on historical data and continuously improved through feedback from actual procedure outcomes, enhancing prediction accuracy over time
Data Source
AI summary
A method for providing a prognosis data record includes receiving a first image data record relating to an examination region of an examination object, and receiving an operating parameter of a medical object that is arranged at the examination region of the examination object and positioning information of the medical object that is arranged at the examination region. The prognosis data record is created by applying a trained function to input data. The input data is based on the first image data record, the at least one operating parameter, and the positioning information of the medical object. At least one parameter of the trained function is based on a comparison with a first comparison image data record. As compared with the first image data record, the first comparison image data record includes changes influenced by the medical object at the examination region. The prognosis data record is provided.