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相关概念视频

Knee Joint01:23

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

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Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
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在两到五年内使用机器学习预测末期膝关节关节炎的出现.

Zubeyir Salis1, Jeffrey B Driban2, Timothy E McAlindon3

  • 1Division of Rheumatology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; School of Human Sciences, the University of Western Australia, Perth, WA, Australia; Centre for Big Data Research in Health, the University of New South Wales, Kensington, NSW, Australia.

Seminars in arthritis and rheumatism
|March 21, 2024
PubMed
概括

一个新的机器学习工具可以在2-5年内准确预测末期膝关节关节炎 (KOA) 的进展,从而提高KOA临床试验的效率. 该工具提供了一种可靠的方法来识别可能患有严重KOA的参与者.

关键词:
医疗保健的质量,获得和评估.学习曲线的学习曲线评估患者的结果评估.技术评估, 生物医学

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科学领域:

  • 整形外科 整形外科 整形外科
  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学

背景情况:

  • 为膝关节骨关节炎 (KOA) 试验确定参与者是一项挑战.
  • 目前使用全膝关节置换 (TKR) 的方法不可靠.
  • 对于严重的KOA需要一个强大的指标,导致最终阶段的KOA (esKOA) 测量.

研究的目的:

  • 开发和验证一种机器学习工具,用于预测esKOA在2-5年内出现.
  • 通过识别有风险的个体来提高KOA临床试验的效率.
  • 为严重的KOA进展提供比TKR更可靠的预测指标.

主要方法:

  • 利用骨关节炎倡议 (OAI) 数据进行模型培训 (3,114名参与者) 和验证 (606名参与者).
  • 外部验证的模型使用多中心骨关节炎研究 (MOST) 数据 (1602名参与者).
  • 根据放射性严重性和症状强度定义esKOA;被认为是51个预测因素.

主要成果:

  • 在外部验证中达到高的曲线下面积 (AUC) 值,用于预测esKOA:右膝盖 (2.5年: 0.847,5年: 0.853),左膝盖 (2.5年: 0.824,5年: 0.807).
  • 具有较少预测因素的模型显示了可比性能的表现.
  • 有一个EsKOA预测的在线工具可用.

结论:

  • 开发了一个强大的,外部验证的机器学习工具,用于预测esKOA的发病.
  • 该工具擅长识别可能在2-5年内发展为esKOA的个人.
  • 这种工具可以显著提高未来KOA临床试验的效率和设计.