VOA Estimation Model for Directional Deep Brain Stimulation Leads
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Solution Overview
Problem
Current models for estimating the volume of tissue activated (VOA) by stimulation in deep brain stimulation therapies are limited by their reliance on restrictive assumptions about leadwire symmetry and require separate models for different leadwire types, leading to inefficiencies and inaccuracies, especially when dealing with directional leadwires.
Innovation Solution
A machine learning-based estimation model that accepts input data of the same type at multiple locations along neural elements, allowing for the generation of VOAs for various leadwire designs, including both cylindrically symmetrical and directional types, without requiring symmetry assumptions, and can automatically adjust to different stimulation settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are used for different leadwire types (cylindrically symmetrical and directional), then model accuracy for specific leadwire types is improved, but device complexity and development burden increase
Solution Approach 1:
The patent applies universality by developing a single machine learning model that can accurately estimate VOA for multiple leadwire types (cylindrically symmetrical and directional) without requiring separate models for each type. The model takes as input the electric field distribution and neural element properties, and outputs VOA estimation that works universally across different leadwire configurations, thereby reducing development burden while maintaining accuracy.
2Device complexity
If restrictive assumptions about leadwire symmetry are made, then model simplicity is improved, but adaptability to different leadwire types deteriorates
Solution Approach 1:
The patent applies parameter changes by using machine learning to capture the essential characteristics of different leadwire types through their electric field distributions and neural element interactions, rather than relying on restrictive symmetry assumptions. The model adapts to different leadwire configurations by learning from training data that includes various leadwire types, enabling it to handle directional and non-directional leadwires with equal accuracy without requiring separate models.
3Adaptability or versatility
If machine learning-based estimation is used, then adaptability to various leadwire designs is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on a comprehensive dataset that includes electric field distributions and VOA calculations for various leadwire types and stimulation parameters. Once trained, the model can quickly estimate VOA for new leadwire configurations without requiring time-consuming computations, as the heavy lifting is done during the training phase. This enables real-time or near-real-time VOA estimation during clinical applications.
Data Source
AI summary
A computer implemented system and method provides a volume of activation (VOA) estimation model that receives as input two or more electric field values of a same or different data type at respective two or more positions of a neural element and determines based on such input an activation status of the neural element. A computer implemented system and method provides a machine learning system that automatically generates a computationally inexpensive VOA estimation model based on output of a computationally expensive system.