机器学习框架减少了对前列腺磁共振成像诊断性能进行系统审查的手工工作量
Andrea Nedelcu1, Benedict Oerther1, Hannes Engel1
1Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
European urology open science
|September 14, 2023
概括
机器学习显著减少了前列腺癌MRI研究的手动查. 这种方法在系统性审查中节省了28%的工作量,加速了前列腺成像报告和数据系统 (PI-RADS) 研究的证据综合.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 证据综合 证据综合
背景情况:
- 前列腺磁共振成像 (MRI) 是前列腺癌诊断的标准.
- 解释遵循前列腺成像报告和数据系统 (PI-RADS).
- 发表的研究的数量需要有效的证据审查.
研究的目的:
- 在系统审查选中研究机器学习,以减少手工工作量.
- 评估机器学习框架对选标题和摘要的效率.
主要方法:
- 在一个积极的学习环境中实现了纯粹的贝叶斯分类器.
- 使用了PI-RADS诊断性能活体系统审查的搜索结果.
- 使用工作保存对采样在95%的识别 (WSS@95%) 的评估性能.
主要成果:
- 在模拟运行中实现了WSS@95%的28% (SE ±0.1%).
- 证明了手动查工作的显著减少.
结论:
- 机器学习框架可以在系统审查中大幅减少手工工作量.
- 这种方法加快了像PI-RADS研究这样的大型证据库的选过程.
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