Cochlear Implant Self-Fitting With Feedback-Based Map Adjustment
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Solution Overview
Problem
Current sensory prosthesis fitting methods are inefficient and resource-intensive, as they rely heavily on clinical measurements during initial sessions, leading to unstable levels and recipient difficulty in identifying perceptions, thus wasting clinical resources.
Innovation Solution
An automated fitting process that uses data analysis to establish initial maps, which are gradually adjusted based on user feedback and environmental data, allowing for efficient and personalized fitting adjustments over time, with clinician intervention triggered by specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated fitting is performed after initial clinical fitting, then productivity is improved by reducing the need for extensive initial clinical sessions, but measurement precision may deteriorate without proper validation mechanisms
Solution Approach 1:
The system performs preliminary automated fitting adjustments before clinical validation, allowing the prosthesis to be pre-configured with initial maps and parameters. This preliminary action reduces the need for extensive clinical adjustment sessions while maintaining accuracy through subsequent validation steps.
Solution Approach 2:
The system implements continuous feedback loops where recipient responses to stimulation queries are collected, logged, and used to automatically adjust fitting parameters. This feedback mechanism ensures that automated fitting maintains precision by continuously validating and refining parameters based on actual recipient perception data.
2Ease of operation
If automated fitting with recipient querying is implemented, then ease of operation is improved by reducing clinician time requirements, but device complexity increases due to additional sensors and processors
Solution Approach 1:
The prosthesis system is designed with multi-functional capabilities, where the same sensors and processors used for primary stimulation also collect fitting data, log recipient responses, and perform automated adjustments. This universal design reduces the need for separate dedicated components, managing complexity while enabling automated operation.
Solution Approach 2:
The system enables self-service fitting capabilities where the prosthesis automatically queries the recipient, collects feedback, and adjusts its own parameters without requiring constant clinician intervention. This self-service approach significantly reduces clinician workload while the automated processes manage the complexity of additional functions.
3Reliability
If fitting progress monitoring and clinician event generation are implemented, then reliability is improved by ensuring appropriate clinical intervention, but loss of time increases due to additional monitoring and communication steps
Solution Approach 1:
The system continuously monitors fitting progress by analyzing logged data and recipient responses, providing real-time feedback on fitting status. When progress thresholds are met or exceeded, the system automatically generates clinician events only when necessary, ensuring reliable outcomes without unnecessary delays. This selective feedback approach maintains reliability while minimizing time loss.
Solution Approach 2:
The system performs preliminary analysis of fitting progress data automatically, pre-processing and evaluating recipient responses before clinician review. This preliminary action filters and prepares data in advance, so when clinician intervention is needed, the information is already organized and analyzed, reducing the time required for clinical decision-making while maintaining reliable outcomes.
Data Source
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AI summary
Newly implanted cochlear implant recipients have highly individual and variable patterns of increasing tolerance and recognition of stimulus. Disclosed techniques include a series of self-administered fittings. During an initial clinical fitting, the recipient is given a basic map. Working at their own pace, the recipient can perform self-administered percept exercises that test the recipient's growing capacity to perceive stimulus quality. The results of the exercises are analyzed and compared with reference data. A clinical alert is generated when the recipient's data pattern conforms to a predetermined criteria that indicates the recipient can benefit from a clinical visit because their performance is of a sufficiently stable nature. Optionally, the results of the exercises can be combined with trained clinical data, to make incremental map adjustments over time as the recipient is adapting to cochlear stimulation at their own pace.