Cochlear Lead Insertion Guidance Using Patient-Specific ML Profiles
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Solution Overview
Problem
The successful insertion of an electrode lead within a cochlea for a cochlear implant is challenging due to factors like cochlea size and shape, residual hearing, and intraoperative decisions, making it difficult for surgeons to optimize insertion speed, angle, and depth.
Innovation Solution
A machine learning model-based system that uses geometric and procedural data to provide predictive intraoperative measurement profiles and recommendations for lead insertion, trained on historical data to account for recipient-specific characteristics and surgeon tendencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a surgeon manually performs lead insertion based on general guidelines, then the procedure can be completed, but the insertion parameters (speed, angle, depth) cannot be optimized for individual patient characteristics
Solution Approach 1:
The system performs preliminary analysis of patient-specific cochlear geometry and residual hearing characteristics before the insertion procedure. The machine learning model pre-calculates optimal insertion parameters (speed, angle, depth) based on the individual patient's anatomical features, allowing the surgeon to execute a pre-planned optimized procedure rather than making decisions without personalized guidance
Solution Approach 2:
The system creates a virtual geometric model (copy) of the patient's specific cochlear structure based on imaging data. This digital replica allows for simulation and optimization of insertion parameters without risking actual patient safety, and the optimized parameters are then transferred to guide the real surgical procedure
2Reliability
If the surgeon adjusts insertion parameters in real-time based on patient characteristics, then insertion success improves, but the time required for decision-making increases
Solution Approach 1:
The machine learning model generates optimal insertion parameter recommendations before the actual insertion begins, based on pre-acquired imaging and patient data. This preliminary optimization eliminates the need for time-consuming intraoperative calculations and decision-making, allowing the surgeon to proceed directly with pre-determined optimal parameters
Solution Approach 2:
The system incorporates real-time feedback mechanisms that monitor insertion progress and compare actual parameters against the optimized target parameters. This feedback loop allows for minor adjustments while maintaining overall adherence to the optimized plan, ensuring high success rates without requiring complete re-planning during the procedure
3Reliability
If the electrode lead is inserted to maximize stimulation coverage, then hearing effectiveness improves, but trauma to intracochlear structures increases
Solution Approach 1:
The system tailors the insertion parameters to the specific local characteristics of each patient's cochlea, including variations in scala tympani geometry, bone density, and residual hair cell locations. Rather than using a one-size-fits-all approach, the machine learning model optimizes insertion speed, angle, and depth for the specific local anatomy, achieving effective electrode placement while minimizing trauma to vulnerable intracochlear structures
Solution Approach 2:
The system identifies and maps critical intracochlear structures (such as the vestibular aqueduct, modiolus, and hair cell regions) before insertion. By having this anatomical map prepared in advance, the optimized insertion path can be planned to deliberately avoid these sensitive structures while still achieving comprehensive electrode coverage for effective stimulation
Data Source
AI summary
An illustrative insertion management system may be configured to provide, as an input to a machine learning model, data associated with a lead insertion procedure in which an electrode lead is inserted to a cochlea of a recipient of a cochlear implant; generate, based on an output of the machine learning model, procedure assistance data configured to assist a user in performing the lead insertion procedure; and set, based on the procedure assistance data, one or more parameters associated with the lead insertion procedure, wherein the setting comprises intraoperatively adjusting the one or more parameters during the lead insertion procedure.