Computer-Aided Femoral Prosthesis Selection Strategy
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Solution Overview
Problem
The current selection of femoral prosthesis replacement strategies is often empirical and lacks optimization, relying on practitioner experience rather than rational or computer-assisted methods, despite existing methods using geometric considerations and material resistance.
Innovation Solution
A computer-assisted method that classifies femoral prosthesis replacement strategies based on shape, bone alterations, bone density, and fixation mode, using a program to identify the optimal strategy by referencing a catalog of configurations and associated strategies, ensuring mechanical strength and durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a computer-assisted method is implemented to select optimal replacement strategies, then the reliability and objectivity of the selection process is improved, but the device complexity and implementation difficulty increases
Solution Approach 1:
The patent segments the complex selection process into distinct classification criteria (bone quality, prosthesis type, fixation mode, patient activity level) that can be independently evaluated and combined. This modular approach improves reliability by systematically considering multiple factors while managing system complexity through structured organization of the decision-making process.
Solution Approach 2:
The patent applies preliminary action by pre-establishing classification criteria, evaluation rules, and decision algorithms before the actual selection process. The system pre-processes and stores reference data about different prosthesis types and replacement strategies, enabling rapid and reliable online decision-making without requiring complex real-time calculations during patient consultations.
2Manufacturing precision
If multiple classification criteria are used to evaluate replacement strategies, then the manufacturing precision and suitability of the selected prosthesis is improved, but the difficulty of detecting and measuring all criteria increases
Solution Approach 1:
The patent applies local quality by tailoring the evaluation criteria and weighting to the specific local conditions of each patient case. Different classification criteria are emphasized based on the particular bone quality, prosthesis type, and patient characteristics, allowing precise selection adapted to local requirements rather than applying a uniform rigid framework.
Solution Approach 2:
The patent introduces a computer-assisted evaluation system as an intermediary between the complex classification criteria and the practitioner's decision-making. This intermediary automatically processes and integrates multiple criteria (bone density, prosthesis geometry, fixation mode), reducing the direct burden on practitioners to evaluate and synthesize all factors manually while maintaining high selection precision.
3Ease of operation
If empirical methods are used for selecting replacement strategies, then the ease of operation is maintained, but the productivity and efficiency of the replacement process deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to automatically evaluate replacement strategies based on input parameters without requiring extensive manual analysis by the practitioner. The computer-assisted method autonomously processes classification criteria, applies evaluation rules, and generates recommendations, freeing practitioners from time-consuming manual assessments while maintaining ease of operation through user-friendly interfaces.
Solution Approach 2:
The patent replaces the mechanical system of manual empirical evaluation with a computer-based information processing system. This substitution automates the analysis of multiple classification criteria and replacement strategies, dramatically improving productivity and efficiency while maintaining ease of operation through automated decision-support tools that guide practitioners through the selection process.
Data Source
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AI summary
The method involves classifying real cases of replacement of above-knee prosthesis observed in different configurations based on previously established classification criterions, where each configuration is defined by a specific set of values or specific instances of the criterions. An optimal replacement strategy from a set of known usable strategies is associated to each configuration by statistical analysis, and the optimal strategy for each new case of replacement of the above-knee prosthesis is obtained based on the values or instances which take the criterions for the new cases. An independent claim is also included for a computer program permitting to select an optimal replacement strategy for above-knee prosthesis from a set of known usable strategies.