Dynamic Knee Score Visualization for TKR Parameter Optimization
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Solution Overview
Problem
Surgeons lack tools to effectively investigate and optimize knee surgery parameters for individual patients, leading to suboptimal pain relief and functional outcomes in Total Knee Replacement (TKR) surgeries, due to limited data and biased decision-making.
Innovation Solution
A method using computer tomography data and machine learning models to generate a graphical representation of a dynamic knee score, allowing surgeons to visualize the impact of tibial rotation and slope on patient outcomes, enabling informed parameter adjustments for improved surgical results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgeons rely on conventional survivorship data and biased decision-making, then mechanically safer surgical decisions are made, but patient outcome optimization is compromised
Solution Approach 1:
The system performs preliminary kinematic simulations and machine learning predictions before surgery to forecast patient outcomes for different surgical parameter choices. This allows surgeons to see predicted outcomes in advance and adjust parameters proactively to optimize both mechanical safety and patient satisfaction, rather than relying on biased post-hoc survivorship data.
Solution Approach 2:
The system incorporates machine learning models trained on historical patient outcomes to provide feedback to surgeons about predicted outcomes for different surgical parameter choices. This feedback loop enables surgeons to adjust their decisions based on predicted patient satisfaction and functional outcomes, not just mechanical safety metrics.
2Manufacturing precision
If surgeons make small changes in surgery parameters to increase success rates, then patient outcomes could be improved, but surgeons lack tools to investigate which parameter changes would have positive impact
Solution Approach 1:
The system introduces a computer-based intermediary tool that performs kinematic simulations and machine learning predictions to bridge the gap between surgical parameter choices and patient outcomes. This intermediary provides surgeons with actionable insights about which parameter changes are likely to improve outcomes, making parameter optimization accessible without requiring extensive expertise or trial-and-error.
Solution Approach 2:
The system replaces the traditional mechanical trial-and-error approach to parameter optimization with computational simulations and machine learning models. Instead of relying on surgeon intuition and experience alone, the system uses virtual kinematic simulations to predict outcomes, substituting computational analysis for mechanical guesswork.
3Ease of manufacture
If conventional survivorship tracking is used, then data collection is easy and widely adopted, but it masks the true extent of patient dissatisfaction and suboptimal outcomes
Solution Approach 1:
The system adds another dimension to outcome measurement by incorporating patient-reported outcomes and functional metrics alongside traditional survivorship data. This multi-dimensional approach captures patient satisfaction and functional improvement, providing a more complete picture of surgical success that doesn't mask dissatisfaction.
Solution Approach 2:
The system transitions from static survivorship tracking to dynamic outcome assessment by continuously collecting and analyzing patient-reported outcomes, functional metrics, and kinematic data over time. This dynamic approach reveals trends and patterns in patient satisfaction and outcomes that static survivorship data cannot capture.
Data Source
AI summary
This disclosure relates to a computer assistant for a surgeon with a graphical representation of a dynamic knee score for a knee surgery. A processor receives computer tomography data of a current patient's knee and user input from the surgeon, the user input comprises an identifier of a knee implant. The processor then retrieves multiple machine learning model parameters indicative of a machine learning performed on historical patient records. For multiple values of a rotation of the tibial component and a slope of the tibial component the processor configures a post-operative kinematic model performs a kinematic simulation and estimates a current patient outcome by applying the machine learning model. Finally, the processor generates a shaded surface spanning the multiple values of a rotation of the tibial component and a slope of the tibial component on a user interface to graphically represent the estimated current patient outcome for each of the rotation and slope.