在ACL重建后的重伤风险:基于人工智能的预测模型的范围审查
Ayla Ahmed1, Jaison Patel1, Kesavan Richard Sri-Ram1
1Department of Trauma and Orthopaedics, Barts Health NHS Trust, London, United Kingdom.
The Knee
|February 10, 2026
概括
在重建后预测前十字带 (ACL) 重伤是具有挑战性的. 虽然人工智能和机器学习 (AI/ML) 模型显示出希望,但目前的证据有限,需要进一步验证临床使用.
科学领域:
- 整形外科手术 整形外科手术
- 生物医学数据科学是生物医学数据科学.
- 运动医学运动医学
背景情况:
- 前十字带 (ACL) 重伤后重建对患者的功能,恢复体育和医疗保健费用构成重大挑战.
- 尽管已知风险因素,对患者特异性ACL重伤的准确预测仍然有限.
研究的目的:
- 系统地审查和综合监督人工智能/机器学习 (AI/ML) 模型的证据,以预测ACL重伤或初级ACL重建后相关不良结果.
- 专注于结果定义,模型验证策略和现有AI/ML模型的临床适用性.
主要方法:
- 对PubMed和Embase数据库进行了全面的范围审查.
- 使用PRISMA-ScR指南定义了资格标准,从而对包括的10项研究进行了定性综合.
主要成果:
- 关于结果定义,数据源,建模技术和验证方法的研究中观察到显著的异质性.
- 通常使用监督组合方法,在开发数据集中显示中等到高的歧视,但主要依赖内部验证和有限的外部验证.
- 关于模型校准和临床实用性的报告不一致,预测因素包括解剖学,患者相关和手术因素.
结论:
- 人工智能/ML模型显示出预测ACL重伤的潜力,但目前的证据基础在方法上是多样化的,主要是发育性的.
- 有限的外部验证,不一致的校准评估和可变的结果定义阻碍了这些模型的临床适用性.
- 现有的AI/ML模型用于ACL重伤预测在这个阶段应该被认为是探索性的.
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