AI Screw Placement Optimization for Long Bone Fracture Fixation
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Solution Overview
Problem
Conventional analysis techniques for optimizing screw numbers and positions in long bone fracture fixation surgeries are inefficient, requiring extensive time and often resulting in lower accuracy due to the inability to comprehensively analyze all possible screw placements.
Innovation Solution
An analysis method utilizing computed tomography (CT) scans and X-ray images to create 3D models of long bones and bone plates, combined with artificial intelligence (AI) to analyze stress distributions and automatically select optimal screw configurations, significantly reducing analysis time and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analysis techniques are used to evaluate all possible screw placements, then comprehensive analysis accuracy is improved, but analysis time increases to several days which is not practical
Solution Approach 1:
The patent segments the comprehensive analysis task into multiple stages: first using AI to pre-select representative screw placement configurations from all possible combinations, then performing detailed finite element analysis only on these selected configurations. This segmentation reduces the analysis scope from exhaustively evaluating all possible screw placements to evaluating only the most promising candidates, thereby maintaining analysis accuracy while dramatically reducing computation time from days to practical durations.
Solution Approach 2:
The patent applies preliminary action by using artificial intelligence to perform preliminary selection and evaluation of screw placement configurations before conducting the full finite element analysis. The AI system pre-processes the data, identifies optimal candidate configurations based on initial criteria, and prepares the analysis framework in advance, so that when the detailed mechanical analysis is performed, it can focus efficiently on the most relevant cases rather than starting from scratch.
2Reliability
If the number of screws is increased to improve fixation stability, then screw loosening is reduced, but bone tissue damage increases
Solution Approach 1:
The patent employs parameter changes by systematically varying the number, position, and configuration of screws as adjustable parameters in the optimization process. The AI-driven system evaluates different parameter combinations (screw counts, placements, orientations) and identifies the optimal set that achieves maximum fixation stability while minimizing bone tissue damage. This allows finding the precise balance point rather than using fixed conventional rules.
Solution Approach 2:
The patent applies local quality by optimizing screw placement at specific local positions along the bone rather than using uniform distribution. The system identifies critical regions that require enhanced fixation and concentrates screws in those areas, while reducing or eliminating screws in regions where they would cause unnecessary bone damage. This localized optimization ensures fixation stability is achieved precisely where needed while minimizing overall bone tissue disruption.
3Productivity
If representative surgical fixation combinations are used for qualitative comparison, then analysis time is reduced, but the ability to comprehensively analyze all screw placements is lost leading to lower accuracy
Solution Approach 1:
The patent introduces an artificial intelligence system as an intermediary between the complete set of possible screw configurations and the final analysis. This AI intermediary intelligently filters and selects the most representative and promising configurations from all possible combinations, ensuring that the subsequent detailed analysis focuses on the most relevant cases. This intermediary layer maintains comprehensive analysis capability by systematically evaluating all options through the AI filter, while still achieving practical analysis times by avoiding exhaustive detailed analysis of every single configuration.
Data Source
AI summary
An analysis method for optimizing number and position of screws used in long bone (fracture fixation) surgery, including the following steps. Perform a CT scan on a surgical patient to obtain a medical image to build a long bone 3D model. Analyze an X-ray image to obtain a long bone fracture condition and a bone quality condition. Select a bone plate 3D model from the model database. Import the bone plate 3D model into the long bone 3D model and set an initial fixation position of the bone plate 3D model. Instruct the first artificial intelligence to comprehensively analyze the conditions to automatically select alternative solutions in the model database. Then, use a computer-aided analysis system to analyze the stress distribution of the alternative solutions, and a first preferred solution is obtained through simulating analysis, thereby excluding incorrect or ineffective surgical plans to reduce the analysis time and patient wait time and increasing the surgery success rate.