Medical Organ State Estimation Using Patient-Specific Simulation
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Solution Overview
Problem
Existing techniques struggle to accurately estimate the state of a subject after treatment based on information before treatment, particularly due to variations in heart shape, movement, and blood flow among individuals.
Innovation Solution
A medical data processing device that includes a first acquisition unit, a calculation unit, and an estimation unit to acquire and process CT images, set parameters, and estimate the state of a biological organ after treatment by considering individual differences, using methods like finite element simulation and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a technique of estimating the state after treatment from the state before treatment is established, then treatment planning accuracy is improved, but individual differences in heart shape, movement, and blood flow make the estimation difficult
Solution Approach 1:
The patent changes parameters by creating multiple candidate models with different structural parameters (valve leaflet thickness, annulus diameter, etc.) to represent individual differences. The system selects the most appropriate model by comparing simulation results with actual measurement data, thereby adapting to individual variations while maintaining estimation accuracy.
Solution Approach 2:
The patent creates simplified copy models (simulation models) that replicate the complex individual-specific heart structures. These copy models incorporate key anatomical features and can be rapidly simulated to estimate post-treatment states without requiring complex individual-specific data, thus resolving the contradiction between accuracy and individual variability.
2Adaptability or versatility
If multiple candidate models with different parameters are created to account for individual differences, then adaptability is improved, but calculation complexity and processing time increase
Solution Approach 1:
The patent applies partial action by creating multiple candidate models only for the most critical parameters that significantly affect treatment outcomes (e.g., valve leaflet thickness, annulus diameter). Not all possible parameters are varied, only those that provide the most value in terms of estimation accuracy, thereby reducing unnecessary computational complexity.
Solution Approach 2:
The patent replaces complex mechanical simulations with simplified computational models that use pre-defined structural parameters. Instead of performing full-scale finite element analysis for each candidate model, the system uses efficient computational algorithms to evaluate multiple models and select the best match, reducing calculation complexity while maintaining adaptability.
3Measurement precision
If simulation models with structural parameters are used to estimate post-treatment states, then estimation accuracy is improved, but the complexity of setting appropriate parameters increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine appropriate structural parameters for simulation models. The system acquires actual measurement data, compares it with simulation results, and autonomously selects the most appropriate candidate model without requiring manual parameter adjustment by clinicians, thereby reducing parameter setting complexity while maintaining high estimation accuracy.
Solution Approach 2:
The patent uses feedback mechanisms where simulation results are continuously compared with actual measurement data. Based on this feedback, the system iteratively refines parameter selection and model choice, automatically converging on the most accurate configuration. This feedback loop eliminates the need for complex manual parameter setting while ensuring high estimation accuracy.
Data Source
AI summary
A medical data processing device according to an embodiment includes processing circuitry. The processing circuitry acquires estimated data related to a state of a biological organ at a first timing, and actually measured data indicating the state of the biological organ at the first timing. The processing circuitry calculates a parameter based on the estimated data and the actually measured data. The processing circuitry estimates a state of the biological organ in a predetermined time phase at a second timing different from the first timing based on the parameter and the estimated data in the predetermined time phase.