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对机器学习和深度学习算法之间的特征进行比较,用于在基于云的新型系统中对废弃部位进行分类

Masataka Narita¹, Daisuke Kawano¹, Naomichi Tanaka¹

¹From the Department of Cardiology, Saitama Medical University, International Medical Center, Saitama, Japan.

|March 19, 2025

概括

与R12.1版本相比,CARTONET R14模型在分析心房移除手术时显示出更高的准确性. 这种深度学习系统增强了灵敏度和积极的预测价值,以获得更好的临床见解.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
人工智能在医学中的应用

背景情况:

CARTONET是一个基于云计算的系统,用于分析CARTO系统的废弃程序.
目前的CARTONET R14模型使用深度学习,但其性能指标需要彻底评估.

研究的目的:

为了比较R12.1和R14模型之间的CARTONET系统的性能特征.
评估CARTONET R14深度学习模型的准确性和预测价值.

主要方法:

对396例心房移除病例的数据进行分析.
调查CARTONET R14自动化解剖定位模型的灵敏度和积极预测值 (PPV).
在CARTONET R12.1和CARTONET R14模型之间的数据比较.

主要成果:

与R12.1模型相比,CARTONET R14模型显著提高了灵敏度 (77.5%) 和PPV (86.2%),分别为71.2%和85.6%).
对39,169个点和625个段的分析显示,肺静脉后部区域重新连接的发生率很高.
在右肺静脉和左肺静脉的屋顶区域观察到重新连接的可能性最高.

结论:

与R12.1模型相比,CARTONET R14模型在灵敏度和PPV方面提供了显著的改进.
在预测潜在的重新连接站点方面,R14模型与R12模型保持着类似的趋势.

关键词:

自动分类的自动分类.卡通网就是一个卡通网.深度学习是一种深度学习.外部验证的验证方法机器学习机器学习肺静脉隔离肺静脉隔离肺静脉隔离

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