Machine-Trained Model Selection For Implant Parameters
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Solution Overview
Problem
Current surgical procedures for implant placement, such as pedicle screw fixation in spine surgery, rely heavily on manual selection and adjustment by surgeons, leading to cognitive load and potential errors due to variability in surgeon preferences and anatomical specifics, which can result in suboptimal implant placement and increased surgical risks.
Innovation Solution
A computer-implemented method for user-specific selection of machine-trained models to determine implant-related parameters, utilizing patient image data and historical user feedback to suggest and refine implant positions and orientations, reducing manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection and adjustment of implant parameters is performed by surgeons, then flexibility to accommodate surgeon preferences and anatomical specifics is improved, but cognitive load increases and potential errors occur due to variability
Solution Approach 1:
The system incorporates feedback mechanisms where surgeons can confirm, reject, or adjust automatically generated implant parameters. The system learns from these feedback interactions to refine its model and improve future recommendations, thereby reducing errors while maintaining flexibility.
Solution Approach 2:
The system performs self-adjustment by automatically generating implant parameters based on patient anatomy and surgeon preferences, reducing the cognitive load on surgeons while maintaining the flexibility needed for individualized treatment plans.
2Loss of time
If automatic determination of implant position is implemented, then time consumption is reduced, but manual revision and adjustment by surgeons is still required
Solution Approach 1:
The system performs preliminary determination of implant parameters automatically before surgeon review. This pre-computation reduces the time surgeons need to spend on manual calculations while maintaining the ability for necessary adjustments.
Solution Approach 2:
The system uses feedback from surgeon adjustments to refine future automatic determinations, gradually reducing the need for manual revision while maintaining accuracy and adaptability.
3Measurement precision
If multiple machine-trained models are provided for different implant parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the complex task of implant parameter determination into multiple specialized models, each trained for specific parameters (e.g., screw position, angle, length). This segmentation improves precision for each parameter while managing complexity through modular architecture.
Solution Approach 2:
The system provides a universal interface that handles multiple implant parameters through a single integrated platform, reducing the practical complexity despite having multiple specialized models underneath.
4Adaptability or versatility
If user-specific model selection is implemented, then adaptability to individual surgeon preferences is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system performs self-service by automatically selecting and applying the appropriate model based on patient anatomy and surgeon preferences without requiring manual intervention, thereby maintaining adaptability while reducing complexity.
Solution Approach 2:
The system uses feedback from surgeon interactions to automatically refine model selection, making the process more accurate and less complex over time as the system learns from actual usage patterns.
Data Source
AI summary
A computer-implemented technique for a user-specific selection of a machine-trained model for determining an implant-related parameter. A set of different machine-trained models is provided. Each model is indicative of a dedicated implant-related parameter for a dedicated patient anatomy. The method may include receiving first patient image data indicative of a patient anatomy in which an implant is to be placed, and applying at least one first model from the set of models on the first patient image data to determine an implant-related parameter. The determined implant-related parameter is suggested to a dedicated user, and feedback is received from the user on the suggested implant-related parameter. The user feedback includes one of a confirmation of the suggested implant-related parameter and an adaptation thereof. At least one second model from the set of models that is to be applied on second patient image data may be selected, based on the user feedback.