Patient-Specific Knee Alignment Planning With JLO and HKA Angles
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Solution Overview
Problem
Existing patient-specific surgical planning in total knee arthroplasty (TKA) faces challenges due to variations in patient anatomy, leading to suboptimal knee function and patient satisfaction, as traditional alignment techniques fail to account for individual anatomical differences.
Innovation Solution
A surgical system and method utilizing a joint line obliquity (JLO) classification system for patient-specific planning, involving preoperative imaging, intraoperative assessments, and data-driven adjustments to JLO and hip-knee-ankle (HKA) angles to achieve optimal implant positioning and soft tissue balance, using regression analysis and three-dimensional modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional alignment techniques are used in TKA, then the surgical procedure is simple and standardized, but the individual anatomical differences are not accounted for, leading to suboptimal knee function
Solution Approach 1:
The system performs preliminary actions by acquiring preoperative imaging data (CT or MRI scans) and generating three-dimensional models of the patient's knee joint before surgery. This allows the surgical plan to be customized in advance based on the patient's specific anatomy, including joint line obliquity and HKA angle measurements, so that individual anatomical differences are accounted for before the actual surgical procedure begins
Solution Approach 2:
The system changes parameters by measuring specific anatomical parameters (joint line obliquity, HKA angle, bone morphology) from the patient's imaging data and using these measured values to customize the surgical plan. The system determines specific resection depths, implant positioning angles, and alignment targets based on the patient's actual anatomical measurements rather than using standardized values
2Manufacturing precision
If patient-specific surgical planning is implemented to account for individual anatomical differences, then alignment accuracy is improved, but the complexity of the surgical system increases
Solution Approach 1:
The system segments the surgical planning process into distinct modules: (1) acquiring preoperative imaging data, (2) generating three-dimensional models of the knee joint, (3) measuring anatomical parameters (JLO, HKA angle), (4) determining surgical corrections needed, and (5) generating a customized surgical plan. This segmentation allows each function to be performed separately and systematically, reducing overall complexity
Solution Approach 2:
The system uses an intermediary computational processing step that takes the patient's imaging data and automatically generates three-dimensional models and measurements. This intermediary system acts as a mediator between the raw imaging data and the surgical plan, performing the complex calculations and visualizations automatically to reduce the burden on surgeons
3Manufacturing precision
If three-dimensional modeling and regression analysis are used to determine optimal alignment, then surgical precision is enhanced, but the time required for surgical planning increases
Solution Approach 1:
The system performs the computationally intensive three-dimensional modeling and regression analysis as a preliminary action during the preoperative planning phase, rather than during the actual surgery. This allows sufficient time for detailed analysis while ensuring the results are ready before the surgical procedure begins, so no surgical time is lost
Solution Approach 2:
The system creates a digital copy (three-dimensional model) of the patient's knee joint from imaging data. This virtual model serves as a replica that can be analyzed, measured, and manipulated computationally without affecting the actual patient. The regression analysis and alignment optimization are performed on this digital copy, and the results are then applied to guide the physical surgical procedure
Data Source
AI summary
Disclosed herein is a method for knee joint balancing. The method can include determining a joint line obliquity of a knee joint and a hip-knee-ankle angle of the knee joint. The method can include comparing the joint line obliquity and the hip-knee-ankle angle to a predetermined value, and adjusting the joint line obliquity and hip-knee-ankle angle of the knee joint if the joint line obliquity and the hip-knee-ankle angle are different from the predetermined value. The joint line obliquity can be any of an extension line joint line obliquity and flexion joint line obliquity.