Neural Network Endovascular Coil Specification
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Solution Overview
Problem
Specifying the correct endovascular coil for aneurysm treatment in coil embolization procedures is challenging due to the variety of coils available and the need for specialized knowledge, particularly for non-spherical aneurysms, which can lead to increased aneurysm wall stress and unstable coil basket formation.
Innovation Solution
A computer-implemented method using a neural network trained on X-ray image data to predict endovascular coil specifications, including parameters and characteristics, to provide optimal coil specifications for aneurysm treatment, which can be used to identify suitable coils for specific aneurysm geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide variety of coils are available to treat different aneurysm types, then the adaptability to different aneurysm geometries is improved, but the complexity of selecting the correct coil increases
Solution Approach 1:
The patent uses X-ray image data as a digital copy representation of the aneurysm geometry. The neural network processes this digital copy to predict optimal coil specifications, eliminating the need for complex manual analysis of the actual aneurysm structure while maintaining accurate adaptation to different geometries.
Solution Approach 2:
The patent replaces the mechanical process of manual coil selection based on physician experience with a computational neural network system. The neural network automatically processes X-ray images and predicts coil parameters, substituting human expertise with an automated intelligent system.
2Measurement precision
If conventional coil specification methods are used, then the ease of operation is maintained, but the measurement precision of coil selection accuracy decreases
Solution Approach 1:
The patent introduces X-ray image data as an intermediary representation between the physical aneurysm and the coil selection process. The neural network processes this intermediary data to generate precise coil specifications, improving accuracy while maintaining ease of operation through automated processing.
Solution Approach 2:
The system performs self-service by automatically processing X-ray images and generating coil specifications without requiring manual intervention. The neural network independently analyzes the aneurysm geometry and predicts optimal coil parameters, eliminating the need for complex manual measurement and selection processes.
3Reliability
If specialized knowledge is required for coil selection, then the reliability of treatment outcomes is improved, but the difficulty of detecting and measuring aneurysm characteristics increases
Solution Approach 1:
The patent replaces the mechanical process of manual analysis of aneurysm characteristics by experienced physicians with an automated neural network system. The neural network processes X-ray images to extract aneurysm characteristics and predict optimal coil specifications, maintaining reliability while reducing the difficulty of detection and measurement.
Solution Approach 2:
The patent uses X-ray image data as a digital copy that captures aneurysm characteristics. The neural network processes this copy to extract geometric features and predict coil parameters, eliminating the need for complex manual measurement while maintaining accurate detection of aneurysm characteristics.
Data Source
AI summary
A computer-implemented method of providing an endovascular coil specification of an endovascular coil for treating an aneurysm in a coil embolization procedure, includes: inputting (S120) X-ray image data (110), comprising one or more X-ray images including an aneurysm (120), into a neural network (130, 230) trained to predict, from the X-ray image data (110), endovascular coil data (140, 150) of an endovascular coil for treating the aneurysm (120); and outputting (S130) the endovascular coil data (140, 150) to provide the endovascular coil specification.