Virtual Organ Models for Personalized Heart Valve Assessment
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Solution Overview
Problem
Current diagnostic procedures for valvular heart disease are expensive, time-consuming, and often inaccurate due to reliance on manual measurements in 2D image planes derived from 4D medical imaging, leading to suboptimal treatment results and increased costs, and clinical decisions are not personalized to individual patients.
Innovation Solution
A method and system using learning-based discriminative distance functions and virtual organ models to generate patient-specific models from medical image data, allowing for the retrieval of similar cases and personalized treatment decisions based on meaningful concepts, such as morphology and function, using relative neighborhood graphs for visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurements in 2D image planes are used for anatomical performance assessment, then diagnostic procedures can be performed with existing tools, but the measurements are error-prone and time-consuming
Solution Approach 1:
The patent replaces manual mechanical measurements with automated computer-based analysis of 4D image data. The system automatically generates virtual organ models and performs measurements, substituting the manual mechanical process with an automated computational system that eliminates human error and reduces time consumption.
Solution Approach 2:
The patent transitions from 2D image plane measurements to 3D/4D virtual organ models. By adding spatial dimensions and temporal information, the system enables more accurate anatomical performance assessment while automating the measurement process, thereby improving precision without increasing time consumption.
2Reliability
If elaborate diagnostic procedures and complex interventions are used for VHD treatment, then comprehensive patient evaluation can be achieved, but treatment costs increase and in-hospital death rate remains high
Solution Approach 1:
The patent performs comprehensive patient evaluation and treatment planning before actual interventions. By generating virtual organ models and simulating treatment outcomes in advance, the system allows clinicians to assess multiple treatment options and select the most appropriate approach, thereby improving treatment reliability while potentially reducing the complexity of actual procedures.
Solution Approach 2:
The patent introduces virtual organ models as an intermediary between diagnostic imaging and treatment decision-making. These models serve as a simulation environment where treatment outcomes can be predicted and evaluated before actual patient intervention, reducing the need for complex trial-and-error approaches and improving treatment reliability.
3Adaptability or versatility
If generic information from clinical guidelines is used for clinical decisions, then standardized care can be provided, but decisions are not personalized to specific patients
Solution Approach 1:
The patent segments the patient population into individual cases with unique virtual organ models. Instead of applying generic guidelines uniformly, the system creates personalized representations for each patient, allowing treatment decisions to be tailored to specific anatomical and functional characteristics while maintaining standardized evaluation protocols.
Solution Approach 2:
The patent creates virtual copies (digital twins) of patient-specific organ models that can be used for simulation and treatment planning. These virtual copies allow personalized treatment decisions to be made by testing different approaches in the virtual model before applying them to the actual patient, enabling customization without requiring complex real-time modifications to the decision support system.
Data Source
AI summary
A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.