Patient-Specific Joint Model Using Fiber-Based Ligament Slack Length
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computer models of joints fail to adequately account for patient-specific variability in morphology and material properties, leading to suboptimal surgical outcomes in treatments like ACL reconstruction and knee arthroplasty, with high failure rates and long-term joint degeneration due to inadequate prediction of kinematics and ligament loading patterns.
Innovation Solution
A patient-specific computational computer model of a joint is developed, incorporating ligaments identified under load at full extension, with each ligament constructed as a set of fibers based on a predefined slack length, using an objective function to determine optimal fiber architecture and simulate joint motion to predict forces and kinematics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computer models are used for joint treatment planning, then the models are simple and easy to use, but they fail to account for patient-specific variability in morphology and material properties, leading to suboptimal surgical outcomes
Solution Approach 1:
The ligament is segmented into multiple fibers (e.g., ACL fibers, PCL fibers, MCL fibers, LCL fibers) with individual slack lengths and force-elongation characteristics. This segmentation allows the model to capture patient-specific variability in ligament morphology and material properties, improving prediction accuracy while maintaining manageable complexity through systematic parameter assignment.
Solution Approach 2:
The model employs variable slack length parameters for different ligament fibers, where each fiber type (ACL, PCL, MCL, LCL) has specific slack length values (e.g., ACL: 30-50mm, PCL: 40-60mm, MCL: 20-40mm, LCL: 20-40mm). These parameter changes enable the model to account for patient-specific anatomical variations while maintaining a structured approach to complexity.
2Reliability
If patient-specific customization of surgical parameters is implemented, then treatment outcomes improve, but the complexity of treatment planning increases
Solution Approach 1:
The computer model provides feedback by predicting kinematics and ligament loading patterns based on input parameters (age, sex, height, weight, activity level). These predictions guide surgical parameter selection (graft size, type, tunnel location), creating a feedback loop that optimizes treatment outcomes while systematicizing the planning process.
Solution Approach 2:
The model systematically varies surgical parameters (graft size, type, tunnel location) based on patient-specific input parameters. This parameter change approach allows customization for each patient while maintaining a structured methodology that reduces planning complexity through algorithmic decision-making.
3Reliability
If conventional treatment approaches are used, then the treatment process is straightforward, but failure rates increase due to inability to predict patient-specific joint behavior
Solution Approach 1:
The model provides quantitative feedback on predicted kinematics and ligament loading that guides treatment decisions. This feedback mechanism improves treatment success rates by enabling evidence-based parameter selection while the automated nature of the feedback reduces planning complexity compared to manual trial-and-error approaches.
Solution Approach 2:
The patent replaces manual mechanical trial-and-error treatment planning with a computational model that uses mathematical equations (force-elongation relationships, slack length calculations) to predict joint behavior. This substitution improves prediction accuracy while the algorithmic approach maintains ease of operation through automated calculations.
Data Source
AI summary
The present invention provides a method for creating a computer model of a patient specific joint for treatment planning. The method includes identifying a ligament of a joint of a patient under a load at a predefined position of the joint. The method further includes constructing, with the use of a computer, a computer model of the joint of the patient having: a first bone model, a second bone model, and a ligament model connecting the first and second bone models corresponding to the identified ligament, wherein the ligament model is constructed as at least one fiber based on a predefined slack length.