3D Shoulder Motion Modeling for Glenohumeral Track Assessment
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Solution Overview
Problem
Current surgical planning for shoulder dislocation is time-intensive, laborious, and prone to inconsistency due to the lack of effective assessment methods for the glenohumeral joint, which affects the decision-making process for procedures like soft tissue repair, bone grafting, and joint replacement.
Innovation Solution
A method involving the use of 3D models of the shoulder anatomy, application of motion data, and generation of virtual objects to visualize and assess the glenohumeral joint, including kinematic data and virtual objects like glenoid projections, to determine joint characteristics and plan surgeries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgeons manually visualize and assess shoulder anatomy using traditional methods, then they can evaluate joint instability and plan surgeries, but the process is time-intensive and laborious
Solution Approach 1:
The patent creates a virtual 3D copy of the patient's shoulder anatomy from imaging data, allowing digital manipulation and assessment without repeated manual analysis of physical images. The virtual model can be rotated, scaled, and examined from multiple angles simultaneously, reducing assessment time while maintaining precision.
Solution Approach 2:
The patent transitions from 2D imaging data to 3D volumetric models, enabling assessment in multiple spatial dimensions simultaneously. This allows visualization of joint anatomy from all angles and evaluation of motion trajectories in three-dimensional space, improving assessment efficiency without sacrificing accuracy.
2Reliability
If surgeons manually analyze kinematic motions and manipulate 3D models, then they can evaluate joint state and identify lesions, but the process is laborious and prone to inconsistency
Solution Approach 1:
The patent implements automated algorithms that independently analyze the virtual 3D models to identify lesions and evaluate joint stability. The system automatically processes imaging data, generates models, performs measurements, and provides assessment results without requiring manual intervention for each step, thereby improving consistency and reducing variability in assessment outcomes.
Solution Approach 2:
The patent incorporates automated feedback mechanisms that continuously validate assessment results against established criteria and allow iterative refinement. The system provides objective measurements and comparisons that feedback to the surgeon, ensuring consistent and reliable assessment while simplifying the overall process through automation.
3Manufacturing precision
If comprehensive assessment of glenohumeral joint is performed to determine surgical suitability, then surgical planning can be accurately determined, but the process becomes more time-consuming
Solution Approach 1:
The patent performs preliminary automated assessment of the virtual 3D model to pre-identify lesions, evaluate joint stability, and determine surgical suitability before the surgeon begins detailed planning. This preliminary automated analysis handles routine measurements and comparisons, allowing the surgeon to focus only on complex decision-making aspects and thereby improving overall planning efficiency without sacrificing precision.
Solution Approach 2:
The patent creates a multi-functional virtual modeling system that simultaneously performs multiple assessment tasks including lesion identification, joint stability evaluation, kinematic motion analysis, and surgical planning. This single integrated system replaces multiple separate manual processes, improving productivity while maintaining comprehensive precision through unified automated analysis.
Data Source
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AI summary
Methods of assessing a patient shoulder anatomy is provided. The method includes receiving one or more 3D models based on the patient shoulder anatomy, applying motion data based on a glenohumeral joint to the one or more 3D models, and determining a track engagement of the glenohumeral joint based on the applied motion data. A computing system programmed to perform these methods is also described.