可靠的深度学习用于冠状动脉疾病检测:一个患者级,统计验证的MRI研究
Christiana Raluca Dănciulescu1, Constantin Renato Ivănescu, Daniel Robert Stănescu
1Doctoral School, University of Medicine and Pharmacy of Craiova, Romania; daniel.stanescu@umfcv.ro.
概括
ResNet50在使用心脏磁共振 (CMR) 影像检测冠状动脉疾病 (CAD) 方面表现出卓越的性能. 这种深度学习模型为自动CAD分类提供了统计验证和可靠的解决方案.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 通过心磁共振成像 (CMR) 准确检测冠状动脉疾病 (CAD) 对于及时诊断和有效的临床管理至关重要.
- 深度学习方法为自动化CAD检测提供了潜力,提高了效率和准确性.
研究的目的:
- 评估和比较DenseNet121和ResNet50深度学习架构的性能和统计稳定性,用于自动化CAD分类.
- 用多参数CMR数据评估这些模型的可靠性.
主要方法:
- 心脏磁共振 (CMR) 图像进行预处理,并在患者层面进行分区.
- 使用平均准确度,AUC-ROC和PR-AUC量化模型性能.
- 统计测试 (沙皮罗-威尔克,布朗-福西) 评估了性能分布和差异均性.
主要成果:
- ResNet50以90.43%的准确性,0.862的AUC-ROC和0.891的PR-AUC实现了卓越的性能.
- 而DenseNet121的准确度较低,为81.72%.
- 统计分析表明非正常的性能分布和模型之间的显著差异差异.
结论:
- ResNet50提供了一种可靠且经过统计验证的方法,用于从CMR成像中检测CAD.
- 该研究强调了现实的预处理和全面的统计测试对于可重复的临床性能估计的重要性.
- 深度学习模型,特别是ResNet50,在推进自动心脏病诊断方面显示出重大前景.
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