TKA Implant Planning Using Surgeon-Specific ML Parameters
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Solution Overview
Problem
Current surgical planning systems for total knee arthroplasty (TKA) rely on manual techniques and lack adequate consideration of patient-specific factors and surgeon preferences, leading to variability in outcomes and increased surgical time.
Innovation Solution
A machine learning-based approach that determines optimized implant parameters using a classification algorithm, incorporating patient data and historical surgeon data to generate precise surgical plans, which can be adjusted by surgeons and executed by robotic aids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual techniques are used for implant placement, then surgeon judgment and flexibility are maintained, but surgical time increases and outcome variability increases
Solution Approach 1:
The system performs preliminary analysis of patient anatomy, deformity, and surgeon preferences before surgery to generate a pre-calculated implant plan. This preliminary action provides a starting point that reduces intraoperative decision-making time while maintaining surgeon flexibility for final adjustments.
Solution Approach 2:
The computational system acts as an intermediary between patient anatomy data and surgeon decision-making, processing complex measurements and providing evidence-based recommendations that guide surgeon judgment without replacing it, thereby reducing variability while preserving flexibility.
2Adaptability or versatility
If traditional surgical planning systems are used, then implant placement can be performed, but they fail to adequately account for surgeon preferences and patient-specific factors
Solution Approach 1:
The system customizes the implant plan by integrating specific local factors including individual surgeon preferences, patient deformity characteristics, range of motion requirements, and anatomical variations. This localized customization ensures the plan adapts to unique case requirements while maintaining overall precision.
Solution Approach 2:
The system dynamically adjusts implant parameters based on multiple interacting factors including surgeon philosophy (mechanical, anatomic, kinematic, or constitutional alignment), patient-specific anatomy, and pre-operative deformity measurements, creating a flexible yet precise planning approach.
3Device complexity
If the initial suggested implant plan is generated without considering surgeon preferences, then the planning process is simplified, but significant adjustments are required by the surgeon
Solution Approach 1:
The system performs preliminary calculations incorporating surgeon preferences and patient factors before surgery, generating a pre optimized implant plan that serves as an accurate starting point. This reduces the need for significant intraoperative adjustments while maintaining system efficiency.
Solution Approach 2:
The system incorporates feedback from surgeon preferences and historical outcome data to continuously refine implant recommendations. This feedback mechanism allows the system to learn from past surgeries and improve future planning accuracy without increasing complexity.
Data Source
AI summary
Systems and methods for improved surgical planning are disclosed herein. A processor may determine an optimized planning group based on planning group definitions and historical surgeon data through the use of a machine learning classification algorithm. The processor may further receive patient data comprising anatomical landmarks and surfaces, pre-operative deformity measurements, range of motion measurements, and gap data. The processor may generate optimized implant parameters based on the optimized planning group and the patient data using a machine learning model. The optimized implant parameters may include size, position, and orientation parameters for each of a femoral implant and a tibial implant. The processor may further generate a surgical plan based on the optimized implant parameters.