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Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

机器学习辅助系统审查在整形外科.

¹Department of Orthopaedics, Leiden University Medical Center, Leiden, the Netherlands.

Journal of orthopaedics

|December 13, 2023

概括

通过高效地检索相关论文,机器学习显著减少了骨科系统审查中的工作量. 这种方法节省了60-70%的手动选工作,在前10%内识别了大多数论文.

科学领域:

医疗信息学医疗信息学
基于证据的医学基于证据的医学.
整形医学研究整形医学研究

背景情况:

系统审查在骨科中至关重要,但是劳动密集型的.
机器学习 (ML) 提供了简化系统审查过程的潜力.
这项研究评估了ML辅助的骨科系统评审性能.

研究的目的:

评估ML辅助系统审查的有效性,以检索相关论文.
在骨科检查中,量化ML与传统方法相比节省的工作量.
确定ML在不同复杂性审查场景 (简单,中间,高级) 中的性能.

主要方法:

使用了系统审查 (ASReview) 软件的积极学习.
ASReview 在之前的三个骨科系统审查中进行了测试.
在20次代中评估了性能,测量了节省工作的百分比 (WSS) 和相关参考识别率 (RRF).

主要成果:

在95%回忆 (WSS@95) 中节省的工作比例从50% (高级) 到72% (轻松/中等) 不等.
在100%回忆 (WSS@100) 中节省的百分比工作范围从37% (高级) 到72% (简单).
在前10%的选记录中 (RRF@10) 确定了相关的引用,比例为58% (高级) 到79% (简单).

关键词:

在ASReview中,我们会对此进行评估.人工智能的人工智能是人工智能.机器学习是机器学习.查检查查检查查检查系统性审查系统性审查

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结论:

在骨科研究中,ML辅助的系统审查是有效的,大大降低了负担.
大多数相关论文在选过程的早期被确定 (在10%以内).
可以实现大量的工作节省 (60-70%),所有相关文件在选后被发现了30-40%.