Automated determination of device settings

An automated fitting process using machine learning and real-time feedback optimizes medical device settings, addressing the inefficiencies of manual fitting by adapting to dynamic hearing changes and ensuring optimal performance.

WO2026047480A1 Publication Date: 2026-03-05COCHLEAR LIMITED
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Patent Information

Application Number
PCT/IB2025/058421
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-08-27
Filing Date
2025-08-20
Publication Date
2026-03-05

AI Technical Summary

Technical Problem

Manual fitting of medical devices, such as cochlear implants, is resource-intensive, time-consuming, and subjective, leading to suboptimal outcomes due to variability in audiologist interpretations and the dynamic nature of hearing, which requires frequent adjustments.

Method used

An automated fitting process using machine learning models, signal processing techniques, and real-time recipient feedback to determine optimized device settings, leveraging population data, objective measures, and subjective performance data to calculate a confidence score and update settings based on additional tests.

Benefits of technology

Enables quick and efficient determination of personalized device settings that adapt to changing hearing abilities, reducing the need for frequent manual adjustments and ensuring optimal therapeutic outcomes.

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Abstract

Presented herein are techniques for fitting a recipient device to a recipient. A priori data is used to determine a first plurality of candidate stimulation setting groups for a recipient device of a recipient based on principal component analysis and system, processor, implant, and patient data. A selected one of the first plurality of candidate stimulation setting groups is instantiated in the recipient device. Measurement data associated with the recipient device is obtained while operating using the first one of the first plurality of candidate stimulation setting groups. A second plurality of candidate stimulation setting groups for the recipient device is determined using the measurement data and a selected one of the second plurality of candidate stimulation setting groups is instantiated in the recipient device. This process is repeated iteratively until a high confidence score is reached.
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