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基于深度学习的自动心脏量化在多变性心肌病:与手动细分的比较研究
Shivam Angiras1, Deb Kumar Boruah1, Pranjal Phukan1
1Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences Guwahati, Assam, India.
Acta medica Lituanica
|February 9, 2026
概括
深度学习软件准确地量化了高性心肌病 (HCM) 患者的心脏功能和心肌上,与手动评估相匹配. 这种自动化方法简化了工作流程,但在复杂的情况下需要验证.
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:
- 增高性心肌病 (HCM) 是一种常见的遗传性心脏病.
- 在HCM中,精确评估左心室 (LV) 功能和腹反 (MR) 是至关重要的.
- 心脏磁共振 (CMR) 是黄金标准,但手动分析是耗时的.
研究的目的:
- 将一个深度学习 (DL) 软件 (SuiteHEART) 与手动细分 (syngo.Via) 进行比较,用于HCM患者的心脏参数量化.
- 评估基于DL的自动心脏细分的准确性和效率.
主要方法:
- 对25名接受CMR的成人HCM患者进行前性研究.
- 使用手动和自动DL细分的LVEF,LVEDV,LVSV,AoF,MR和PG的量化.
- 统计分析包括相关性和布兰德-阿尔特曼分析.
主要成果:
- 在所有评估参数 (r=0.81-0.91,p<0.001) 中,在DL和手动测量之间发现了强烈的相关性.
- 布兰德-阿尔特曼分析显示了可接受的协议,没有显著的偏见.
- 自动化细分显著减少了后处理时间 (p<0.001).
结论:
- 基于DL的完全自动化量化准确评估HCM患者的LV功能,MR和流量.
- DL算法可以简化心脏成像分析的临床工作流程.
- 复杂的HCM病例需要进一步验证.
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