循环负荷活动诱发膝骨关节炎的预后。基于模型的分析。

IF 1.6 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-01-27 DOI:10.1080/10255842.2025.2456993

Fatima Zahra Mekrane, Radouane Ouladsine, Abdelwahed Barkaoui, Raymond Ghandour

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引用次数: 0

摘要

日常活动中对膝关节的重复性机械应力在关节软骨（AC）中积累疲劳损伤，导致磨损和膝关节骨关节炎（KOA）。有效的治疗方法仍然有限，这强调了早期识别KOA的预测方法的必要性。本研究提出一个数学模型来估计步行循环负荷下的交流退化。通过整合Miner规则、蒙特卡罗模拟和威布尔分布，该模型预测了AC损失60%体积（一个关键的KOA阈值）之前的剩余周期。根据一个完善的模型进行验证，它提供了预防或延迟KOA进展的个性化策略的潜力。

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Prognostics of the knee osteoarthritis induced by cyclic loading activities. A model-based analysis.

Repetitive mechanical stresses on the knee joint during daily activities accumulate fatigue damage in the articular cartilage (AC), leading to wear and knee osteoarthritis (KOA). Effective treatments remain limited, underscoring the need for predictive approaches to identify KOA early. This study proposes a mathematical model to estimate AC degradation under cyclic loading from walking. By integrating Miner's rule, Monte Carlo simulations, and Weibull distributions, the model predicts remaining cycles before AC loses 60% of its volume, a critical KOA threshold. Verified against a well-established model, it offers potential for personalized strategies to prevent or delay KOA progression.

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.