Pedicle Screw Selection via Finite Element Loading Factors
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Solution Overview
Problem
Current methods for predicting pedicle screw loosening in spinal fixation surgeries lack reliability and fail to translate into improved patient outcomes, as they primarily focus on local assessments of pull-out forces rather than holistic analysis of the entire reinforcing structure.
Innovation Solution
A computer-implemented method that generates 3D models of the spine and reinforcement structures, constructs a finite element model representing the pedicle screws as part of a reinforcement structure attached to vertebrae, applies load in a caudo-cranial direction, and outputs an assessment of different pedicle screw types based on calculated loading factors to minimize the risk of loosening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element models are used to assess pedicle screw loosening risk, then measurement precision of screw stability is improved, but device complexity and difficulty of translation to clinical practice increase
Solution Approach 1:
The finite element model is segmented into distinct components: spinal 3D model, reinforcement structure 3D model, and finite element model. This segmentation allows each component to be generated and validated independently, reducing overall system complexity while maintaining assessment precision.
Solution Approach 2:
The patent introduces an intermediary assessment framework that translates complex finite element analysis results into clinically relevant loading factors and risk assessments. This intermediary layer bridges the gap between sophisticated modeling and practical surgical decision-making.
2Ease of operation
If local pull-out force assessment is used, then ease of operation is improved, but reliability of loosening prediction deteriorates
Solution Approach 1:
The patent merges local pull-out force assessment with global structural analysis by integrating the pedicle screw into the reinforcement structure 3D model. This combination allows the system to evaluate both individual screw properties and their interaction with the overall spinal reinforcement structure, improving prediction reliability while maintaining operational feasibility through automated analysis.
Solution Approach 2:
The finite element model serves multiple functions: it assesses pull-out forces, evaluates structural integrity, determines loading factors, and predicts loosening risk. This multi-functionality eliminates the need for separate assessment methods, improving reliability without proportionally increasing operational complexity.
3Reliability
If holistic analysis of reinforcement structure is performed, then reliability of screw selection is improved, but measurement precision and computational requirements increase
Solution Approach 1:
The patent transforms the complex holistic analysis into quantifiable parameters, specifically loading factors that capture the mechanical stress distribution across the reinforcement structure. By changing the representation from complex stress fields to standardized loading factor parameters, the system maintains high reliability while improving measurement precision through standardized metrics.
Data Source
AI summary
Computer implemented method, computing device, system and computer pro-gram product to support selection between different types of pedicle screws (201-m) to be applied in a spine (202) comprising: generating a spinal 3D-model (MSpine) of a patient; generating a reinforcement 3D-model (MRod) of a reinforce-5 ment structure (30) for interconnecting vertebrae (2001-n) of the spine (202); generating a finite element model (FE) corresponding to the spinal 3D-model (MSpine) and the reinforcement 3D-model (MRod); applying a load onto the finite element model (FE) and determining loading factor(s) of the pedicle screws (201-m) using the finite element model (FE); and outputting an assessment of the plurality of different types of pedicle screws (201-m) based on the calculated loading factor(s).