Automated Deep Brain Stimulation Programming via Machine Learning
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems require manual, time-consuming programming by trained clinicians, involving multiple visits and clinical tests, which can exhaust patients and reduce the accuracy and efficiency of tremor evaluation.
Innovation Solution
An automated system using machine learning techniques to determine optimal stimulation parameters by generating patient-specific response models from objective metrics, allowing for efficient and accurate programming of neurostimulation devices with reduced clinician involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual programming is used by trained clinicians, then the accuracy of stimulation parameter selection is improved, but the time required and patient exhaustion increase significantly
Solution Approach 1:
The patent introduces an automated programming system that acts as an intermediary between the clinician and the DBS device. This system uses machine learning models trained on clinical data to automatically determine optimal stimulation parameters, reducing the time required while maintaining accuracy through algorithmic optimization rather than manual trial-and-error
Solution Approach 2:
The system enables the DBS programming process to partially self-service by using automated algorithms to select parameters based on patient-specific data. The machine learning model independently analyzes clinical metrics and determines optimal settings without requiring continuous clinician intervention, thereby reducing both time and patient exhaustion
2Measurement precision
If multiple clinical tests are administered during programming, then the accuracy of tremor evaluation is improved, but patient exhaustion increases and may reduce test quality
Solution Approach 1:
The system applies partial action by using a reduced set of clinical tests combined with machine learning inference to achieve accurate tremor evaluation. Instead of administering all possible tests, the system selects and combines a subset of tests with automated analysis to reach the same evaluation accuracy with fewer patient burdens
Solution Approach 2:
The system implements continuous feedback loops where automated algorithms analyze test results in real-time and adjust subsequent testing strategies. This allows the system to identify the minimum necessary tests required for accurate evaluation, reducing patient exhaustion while maintaining measurement precision through adaptive, data-driven decision-making
3Ease of operation
If automated brute-force methods are used for programming, then the expertise requirement is reduced, but the number of tests required remains high
Solution Approach 1:
The system transforms the programming approach by changing from brute-force parameter testing to intelligent parameter prediction using machine learning. The model uses patient-specific data to predict optimal parameters directly, eliminating the need for extensive systematic testing while maintaining ease of operation through automated computation
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on extensive clinical datasets before patient programming. This preliminary training enables the system to make informed parameter selections without requiring extensive real-time testing, thereby reducing the number of tests needed while maintaining programming simplicity
Data Source
AI summary
The systems and methods can automatically determine a patient-specific set of stimulation parameters using optimization for exploring and/or programming a neurostimulation device, e.g., deep brain stimulation (DBS). In one implementation, the method may include receiving patient data and set of stimulation parameters associated stimulation delivered to a patient by a neurostimulation device. The method may include determining one or more objective metrics using the patient data for each set of stimulation parameters. The one or more objective metrics may be a quantitative value that represents a rating or score of tremor severity and/or side effect severity. The method may further include generating a patient specific response model using the one or more objective metrics and the associated set of stimulation parameters. The method may also include applying an optimization algorithm to the generated response model to determine a candidate set of one or more stimulation parameters.