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深度学习的应用用于冠状动脉的细分和分类

Şerife Kaba¹, Huseyin Haci², Ali Isin³

¹Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey.

Diagnostics (Basel, Switzerland)

|July 14, 2023

概括

这项研究引入了用于冠状动脉疾病 (CAD) 检测的深度学习. U-Net在动脉细分方面表现出色,而DenseNet201准确地分类了狭窄,帮助心脏病专家并减少了错误.

科学领域:

心脏病学心脏病学
医学成像分析医学成像分析
医疗保健中的人工智能

背景情况:

冠状动脉疾病 (CAD) 是全球主要的死亡原因.
准确检测冠状动脉狭窄症对于有效治疗至关重要.
目前对冠状动脉血管学的手动解释容易导致高工作负载,延迟和人为错误.

研究的目的:

开发和评估深度学习模型,用于冠状动脉的自动细分和分类.
为了比较U-Net,ResUNet-a和UNet++在冠状动脉细分方面的性能.
评估DenseNet201,EfficientNet-B0,Mobilenet-v2,ResNet101和Xception在冠状动脉狭窄症分类中的有效性.

主要方法:

使用U-Net,ResUNet-a和UNet++进行了冠状动脉细分.
使用DenseNet201,EfficientNet-B0,Mobilenet-v2,ResNet101和Xception进行了狭窄症的分类. 这些类型包括:
模型性能使用诸如子得分,贾卡德指数,准确性,特异性,PPV,科恩卡帕和AUC等指标进行评估.

主要成果:

U-Net获得了最高的细分表现,得分为0.8467的子和0.7454的Jaccard指数.
丹森网201表现出优异的分类性能,准确度为0.9000,特异性为0.9833,PPV为0.9556,科恩卡帕为0.7746和AUC为0.9694.

结论:

自动化的深度学习方法显著提高了冠状动脉狭窄检测的准确性和效率.
U-Net是冠状动脉细分的一个高度有效的模型.
DenseNet201显示了冠状动脉狭窄症可靠分类的巨大潜力,有助于临床决策.

关键词:

这就是U-Net.血管造影 - 血管造影这是冠状动脉的冠状动脉.冠状动脉疾病 (CAD) 是一种冠状动脉疾病.预先训练的模型.

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