Model-Based Cochlear Implant Programming for Patient-Specific Neural Health
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Solution Overview
Problem
Current cochlear implant programming methods lack the ability to reliably estimate patient-specific electro-neural interfaces, leading to sub-optimal stimulation settings and variable outcomes for cochlear implant recipients, with many individuals experiencing poor speech understanding and limited restoration of auditory fidelity.
Innovation Solution
A method and system for model-based cochlear implant programming (MOCIP) that involves localizing the electrode array and intracochlear structures, generating a patient-specific electric field model, and establishing an auditory nerve fiber bundle model to estimate neural health, using micro computed tomography images and finite difference models to optimize tissue resistivity and simulate electric fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic electrode positioning and stimulation strategies are used, then device complexity is reduced and ease of operation is improved, but manufacturing precision (electrode positioning accuracy) and reliability (hearing outcome consistency) deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically localizing the electrode array and intracochlear structures using pre-processing algorithms before stimulation programming begins. This pre-localization establishes accurate patient-specific anatomical models that enable precise stimulation settings without requiring complex manual programming, thus improving both positioning precision and ease of operation.
Solution Approach 2:
The invention creates a virtual copy (digital model) of the patient's cochlear anatomy and electrode positioning through image processing. This virtual model serves as a template for optimizing stimulation parameters, allowing precise control of electrode activation patterns without physically manipulating complex anatomical structures, thereby achieving high positioning precision with simplified operation.
2Reliability
If patient-specific anatomical modeling is performed, then manufacturing precision (electrode positioning accuracy) and reliability (hearing outcome consistency) are improved, but device complexity and processing time increase
Solution Approach 1:
The complex anatomical modeling process is segmented into distinct modules: image acquisition, automatic localization of electrode array and intracochlear structures, generation of electrical field models, and optimization of stimulation parameters. Each module processes specific data independently, making the overall complex system more manageable and computationally efficient while maintaining high reliability through specialized processing at each stage.
Solution Approach 2:
The system replaces complex manual programming procedures with automated computational algorithms. Instead of requiring audiologists to manually adjust numerous stimulation parameters based on trial-and-error testing, the system uses computer-based modeling and simulation to automatically determine optimal settings, reducing operational complexity while improving outcome consistency through precise, repeatable calculations.
3Ease of operation
If traditional trial-and-error programming is used, then ease of operation is improved, but measurement precision (neural health assessment accuracy) and reliability (speech recognition outcomes) deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the generated electrical field models and neural health estimates are continuously refined based on patient response data. The model compares predicted neural activation patterns with actual patient outcomes and adjusts stimulation parameters accordingly, enabling precise measurement of neural health while maintaining ease of operation through automated iterative optimization rather than manual trial-and-error.
Solution Approach 2:
Traditional manual trial-and-error programming is replaced with automated computational modeling that uses electrical field simulations and neural health algorithms to precisely assess and optimize stimulation settings. This substitution enables accurate measurement of neural health parameters through objective computational assessment rather than subjective clinical judgment, while the automation maintains ease of operation by eliminating repetitive manual adjustments.
4Reliability
If comprehensive neural health estimation is performed, then reliability (hearing outcome consistency) is improved, but measurement precision requirements and processing complexity increase
Solution Approach 1:
Instead of attempting to measure every possible neural parameter with extreme precision, the system applies partial action by focusing computational resources on the most critical neural health indicators that directly impact hearing outcomes. The model estimates key parameters such as neural activation thresholds, fiber bundle integrity, and electrode-neural interface characteristics, providing sufficient precision for reliable outcome prediction without the excessive complexity of measuring all possible neural properties.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for personalized stimulation settings that improve hearing outcomes by accurately accounting for individual anatomical and neural health variations, reducing channel overlap and interaction artifacts, and enhancing speech recognition and fidelity.
Implementation Method 1
generating a CI electric field model based on the patient-specific electrodes positions of the CI and the patient-specific anatomy shape
Implementation Method 2
optimize tissue resistivity and simulate electric fields
Data Source
AI summary
Systems and methods are provided for performing model-based cochlear implant programming (MOCIP) on a living subject with a cochlear implant (CI) to determine stimulation settings of a patient-customized electro-neural interface (ENI) model. The method includes: localizing an electrode array of the CI and intracochlear structures of the living subject to determine patient-specific electrode positions of the CI and a patient-specific anatomy shape; generating a CI electric field model based on the patient-specific electrodes positions of the CI and the patient-specific anatomy shape; and establishing an auditory nerve fiber (ANF) bundle model using the CI electric field model, and estimating neural health of the living subject using the ANF bundle model applications of the same.