ViViEchoformer:深度视频回归器预测弹射分数
Taymaz Akan1, Sait Alp2, Md Shenuarin Bhuiyan3
1Department of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA 71103, USA.
medRxiv : the preprint server for health sciences
|July 1, 2024
概括
一个新的深度学习模型,ViViEchoformer,从心声图视频中准确测量左心室喷射率 (LVEF). 这种自动化方法为评估心脏功能和诊断心力衰竭提供了人类解释的可靠替代方案.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心脏病是全球主要的死亡原因.
- 准确的射出分数 (EF) 测量对于评估心脏功能和患者的结果至关重要.
- 人类对回声心电图进行EF测量的解释具有固有的可变性.
研究的目的:
- 介绍ViViEchoformer,这是一个使用视频视觉转换器的深度学习模型.
- 为了从心声图视频中完全自动和准确地预测左心室喷射率 (LVEF).
- 为手动心声图解释提供可靠的替代方案.
主要方法:
- 开发了ViViEchoformer,一种使用视频视觉转换器的深度学习方法.
- 在斯坦福大学医院的10,030个角四腔心声回声录像视频上训练了模型.
- 该模型提取时空符号以捕获空间信息和框架间的关系.
主要成果:
- 对于EF预测,ViViEchoformer的平均绝对误差为6.14%.
- 该模型在预测心力衰竭与减少喷射分数 (HFrEF) 中表现出强的表现,AUC为0.83.
- 实现了87%的分类准确度,用于识别HFrEF.
结论:
- ViViEchoformer提供精确的,自动化的左心室功能量化来自心声图视频.
- 该模型为人类评估提供了可靠和准确的替代方案,减少了读者差异.
- 建立了一个基础工具,以加强临床实践中的心声回声图解读.
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