Patient-Specific Tibial Implant Kinematic Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current tibial implants in knee replacement surgeries often fail to account for individual kinematic patterns, leading to issues like stiffness, loss of range of motion, and instability due to mismatched pivot centers and kinematic patterns.
Innovation Solution
A method involving data collection on the tibia's position relative to the femur throughout a range of motion to determine a patient-specific kinematic pattern, which is then used to precisely position and orient a tibial implant, ensuring alignment with the patient's natural pivot point and kinematic characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a tibial implant is designed with a fixed kinematic pattern (e.g., medial pivot), then the implant can be manufactured with standardized geometry, but it cannot accommodate individual patients with different kinematic patterns (lateral pivot, central pivot, etc.), leading to mismatched pivot centers and compromised performance
Solution Approach 1:
The patent applies dynamics by transitioning from fixed, standardized implant geometries to dynamic, patient-specific implant designs. The system collects kinematic data during range of motion, analyzes it to determine individual pivot centers and patterns, and uses this information to dynamically generate customized implant geometries. This allows the implant to adapt to each patient's unique kinematic behavior rather than forcing a standardized pattern.
Solution Approach 2:
The patent changes key geometric parameters of the tibial implant based on patient-specific kinematic analysis. By determining individual pivot center locations and kinematic patterns through range of motion data, the system modifies implant parameters such as pivot center position, articular surface geometry, and resection lines to match each patient's unique requirements, thereby resolving the contradiction between standardization and customization.
2Reliability
If a tibial implant is designed to match a specific kinematic pattern, then the implant can provide optimal performance for that pattern, but it causes stiffness, loss of range of motion, pain, and instability when the patient's actual kinematics do not match the implant design
Solution Approach 1:
The patent implements feedback by collecting actual kinematic data from the patient during range of motion, analyzing this data to determine the patient's true pivot center and kinematic pattern, and then using this feedback information to design the implant. This closed-loop approach ensures the implant is tailored to match the patient's actual biomechanics, eliminating mismatches that cause stiffness, pain, and instability while maintaining optimal performance.
Solution Approach 2:
The patent applies preliminary action by performing kinematic analysis and determining the optimal implant design before the actual surgery. The system collects range of motion data, analyzes it to identify pivot centers and kinematic patterns, and generates the customized implant plan in advance. This preliminary customization ensures the implant will match the patient's actual kinematics, preventing postoperative complications before they occur.
3Device complexity
If the implant pivot center is positioned to match a standardized pattern, then the implant design is simplified, but it fails to account for the individual patient's actual pivot center location, resulting in poor alignment and functional compromise
Solution Approach 1:
The patent replaces traditional mechanical measurement methods with a digital imaging and computational analysis system. Instead of relying on physical trial implants or manual measurement, the system uses range of motion data captured through imaging or sensors, processes this data through computational algorithms to precisely determine pivot center locations, and generates customized implant designs based on these precise measurements. This substitution dramatically improves measurement precision while managing design complexity through automation.
Data Source
AI summary
In one embodiment, a method of planning implantation of a knee prosthesis is performed. Data is collected that is representative of a position of a tibia relative to a femur at a plurality of positions of the tibia relative to the femur through a range of motion of the knee. The data at each position of the plurality of positions includes: a medial maximum convergence of a low point on a medial femoral condyle and a medial tibial articular surface of the tibia and a lateral maximum convergence of a low point on a lateral femoral condyle and a lateral tibial articular surface of the tibia. The medial and lateral convergence locations from the plurality of positions are analyzed to determine a kinematic pattern that is then used to determine an implant position and an implant orientation for a tibial implant to be placed on a resected tibial surface.