Patient-Specific Virtual Pericardial Implant Sizing

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Current pre-operative planning tools for transcatheter valve treatments do not provide adequate insights into the interaction between the implant device and specific patient anatomy, making it difficult to predict complications such as regurgitation and requiring longer processing times.

Innovation Solution

A computer-implemented method that selects the optimum size and optionally predicts the optimum deployment position of a cardiac implant by using patient-specific three-dimensional images and implant models, taking into account cardiac implant deployment and deformation of the cardiac region, to minimize complications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If current pre-operative planning tools are used to determine implant size and anatomy, then basic anatomical measurements can be obtained, but insights into the interaction between implant device and specific patient anatomy are not provided, making it difficult to predict complications

Engineering Contradiction:
Improveinsights into implant-device anatomy interactionVSAvoidprediction of complications
Core Design Contradiction:
Loss of informationVSReliability

Solution Approach 1:

The patent creates a virtual copy of the patient's heart anatomy using 3D imaging data (CT or MRI) to generate a digital twin. This virtual model allows simulation of implant deployment without physical risk, enabling prediction of complications by observing the virtual implant-anatomy interaction before actual surgery.

Inventive Principle:
Principle #26Copying

Solution Approach 2:

The system performs preliminary virtual deployment of the implant model into the patient-specific anatomical model before the actual surgical procedure. This pre-operative simulation allows assessment of potential complications such as regurgitation, coronary obstruction, or conduction abnormalities in advance, enabling informed decision-making about implant size and positioning.

Inventive Principle:
Principle #10Preliminary action

2Measurement precision

If extensive calculations and simulations are performed to predict implant interaction and complications, then more accurate predictions can be made, but processing times increase

Engineering Contradiction:
Improveprediction accuracy of implant interactionVSAvoidprocessing time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The system performs all necessary calculations, simulations, and predictions in advance during the pre-operative planning phase. By completing these computationally intensive tasks before surgery, the actual surgical procedure can proceed without time-consuming real-time calculations, thus reducing overall processing time while maintaining high prediction accuracy.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent uses an intermediary computational model that simplifies the complex interaction between the implant and patient anatomy. This intermediate representation allows for rapid predictions without requiring full complex simulations during critical time periods, balancing accuracy with processing speed.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentUS20250134597A1Method and system for patient-specific virtual percutaneous structural heart intervention
Publication Date: 2025.05.01 FEOPS NV
  • US20250134597A1 patent drawing
  • US20250134597A1 patent drawing
  • US20250134597A1 patent drawing

AI summary

A system and method for selecting, from a series of cardiac implants having different sizes, the cardiac implant having optimum size for implantation in a patient. The method includes obtaining data representative of a patient-specific cardiac region and predicting the optimum size of the cardiac implant best matching a predefined criterion when deployed in the cardiac region. The predicting includes querying a database; determining parameter values for a parametric model representation of the patient-specific cardiac region; and/or entering the data representative of the patient-specific cardiac region into an artificial intelligence device.