PREDICTING PROXIMAL FEMORAL REMODELING AFTER SHORT-STEM HIP ARTHROPLASTY: MACHINE LEARNING–AUGMENTED BIOMECHANICAL MODEL

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00384906" target="_blank" >RIV/68407700:21220/25:00384906 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Jazyk výsledku
angličtina
Název v původním jazyce
PREDICTING PROXIMAL FEMORAL REMODELING AFTER SHORT-STEM HIP ARTHROPLASTY: MACHINE LEARNING–AUGMENTED BIOMECHANICAL MODEL
Popis výsledku v původním jazyce
This study presents a novel hybrid approach that integrates biomechanical modeling with machine learning to evaluate the remodelling of three types of short femoral stems used in THA. The core of the biomechanical analysis was based on the finite element method, which was employed to simulate physiological loading conditions and extract mechanical stimuli. These biomechanical output served as the primary input feature for a machine learning model. The proposed framework enables more efficient optimization of implant designs.
Název v anglickém jazyce
PREDICTING PROXIMAL FEMORAL REMODELING AFTER SHORT-STEM HIP ARTHROPLASTY: MACHINE LEARNING–AUGMENTED BIOMECHANICAL MODEL
Popis výsledku anglicky
This study presents a novel hybrid approach that integrates biomechanical modeling with machine learning to evaluate the remodelling of three types of short femoral stems used in THA. The core of the biomechanical analysis was based on the finite element method, which was employed to simulate physiological loading conditions and extract mechanical stimuli. These biomechanical output served as the primary input feature for a machine learning model. The proposed framework enables more efficient optimization of implant designs.

Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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