Hollow Organ Deformation Simulation Using Machine Learning
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Solution Overview
Problem
Current deformation simulation methods for hollow organs during medical interventions, such as aortic aneurysm repair, are complex and require experimental determination of tissue and instrument properties, making them inefficient and error-prone, especially in handling non-homogeneous deformations caused by stiff instruments in calcified areas.
Innovation Solution
A method utilizing a pre-trained machine-learning algorithm that includes 3D medical recordings of hollow organs and surrounding tissues, segmenting and modeling the organ, and simulating deformations based on tissue properties and instrument characteristics to provide a realistic and automatic deformation simulation, accounting for the influence of calcifications and tissue stiffness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deformation simulation methods are used, then deformation prediction can be achieved, but the process becomes complex and requires experimental determination of tissue and instrument properties
Solution Approach 1:
The patent replaces traditional mechanical/physical simulation methods with a machine learning-based computational approach. Instead of using complex finite element analysis and experimental determination of tissue properties, the system uses pre-trained neural networks that have learned deformation patterns from training data, thereby simplifying the simulation process while maintaining accuracy
Solution Approach 2:
The system performs preliminary training of the machine learning model using pre-labeled deformation data before actual use. This pre-training phase allows the model to capture deformation patterns in advance, so that during actual surgical simulation, the system can quickly predict deformations without requiring real-time experimental determination of tissue properties
2Measurement precision
If experimental determination of tissue properties is performed, then accurate deformation simulation can be achieved, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent creates a digital copy of the patient's anatomy using 3D imaging data (CT or MRI scans) to generate a virtual model. This virtual model replaces the need for physical experimental measurements of tissue properties, as the machine learning model has been trained to predict deformations based on the virtual model's geometric and material characteristics, thereby eliminating time-consuming experimental procedures
Solution Approach 2:
The system substitutes physical experimental measurements with computational machine learning predictions. Instead of physically measuring tissue stiffness and other properties in the laboratory, the system uses pre-trained neural networks that infer tissue properties from imaging data and predict deformations accordingly, significantly reducing measurement time
3Ease of operation
If pre-trained machine-learning algorithm is used, then simulation simplicity and robustness are improved, but the algorithm requires extensive training data and computational resources
Solution Approach 1:
The machine learning model is pre-trained in advance using a comprehensive dataset of deformation cases before actual use. This preliminary training phase allows the model to internalize deformation patterns and tissue behavior, so that during actual surgical simulation, the system operates simply by inputting patient-specific data without requiring extensive computational resources or training data at the time of use
Data Source
AI summary
A system and method for deformation simulation of a hollow organ able to be deformed by the introduction of a medical instrument. The method includes provision of a pre-trained machine-learning algorithm, provision of a 3D medical recording of the hollow organ with surrounding tissue, with the 3D recording having been recorded before the introduction of a medical instrument, segmentation or provision of a segmentation of the 3D medical recording of the hollow organ and establishment or provision of a three-dimensional model of the hollow organ, provision of information about a medical instrument introduced or to be introduced, and simulation of the deformation of the hollow organ to be expected from introduction of the instrument on the basis of the segmented 3D medical recording of the hollow organ and of the surrounding tissue and of the information about the instrument by using the pre-trained machine-learning algorithm.