机器学习用于使用心血管磁共振诊断心肌炎:系统性审查,诊断测试准确性元分析,与人类医生进行比较
Paweł Łajczak1, Oguz Kagan Sahin2, Jakub Matyja3
1Medical University of Silesia, Katowice, Poland. pawel.lajczak03@outlook.com.
The international journal of cardiovascular imaging
|September 9, 2025
概括
机器学习 (ML) 模型显著改善了使用心血管磁共振成像 (CMR) 诊断心肌炎的诊断,显示了与经验丰富的医生可比的高准确性. 需要进一步的研究来应对实施挑战.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 心肌炎是一种心脏组织的炎症,使用心血管磁共振成像 (CMR) 进行诊断.
- 解释CMR用于心肌炎诊断对缺乏经验的医生来说是一个挑战.
- 机器学习 (ML) 模型在提高医疗条件的诊断准确性方面表现有前途.
研究的目的:
- 系统地评估ML模型的诊断准确性,通过CMR识别心肌炎.
- 将ML模型的性能与人类医生解释进行比较.
主要方法:
- 在主要数据库 (PubMed,Embase,科学网,Cochrane,Scopus) 进行了系统的文献搜索.
- 包括报告ML用于使用CMR检测心肌炎的诊断准确性的研究.
- 用一个随机效应模型来估计聚合的诊断准确度指标.
主要成果:
- 分析了12项研究中的141个ML模型结果.
- 最好的ML模型显示出高灵敏度 (0.93) 和特异性 (0.95).
- 曲线下的聚合面积 (AUC) 为0.97,表明出色的诊断性能,与人类医生相比.
结论:
- 用ML增强的CMR为心肌炎提供了高的诊断准确性,可能超过新手放射科医生的诊断准确性.
- 临床实施可能受到异质性,质量问题以及缺乏成本效益数据的限制.
- 未来的研究应该专注于ML方法的成本效益和偏差降低.
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