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基于视频的人工智能对心脏功能进行节拍评估
David Ouyang1, Bryan He2, Amirata Ghorbani3
1Department of Medicine, Stanford University, Stanford, CA, USA. ouyangd@stanford.edu.
Nature
|April 10, 2020
概括
一个新的深度学习算法EchoNet-Dynamic准确地从心声图视频中评估心脏功能,
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 准确的心脏功能评估对于诊断心血管疾病,查心脏毒性以及管理危急病患者至关重要.
- 目前的人类评估方法依赖于有限的心脏循环采样,并表现出显著的观察者间变异性.
研究的目的:
- 开发和验证深度学习算法EchoNet-Dynamic,用于使用心声图视频进行自动心脏功能评估.
- 在准确性,可复制性和效率方面克服人类评估的局限性.
主要方法:
- 在大量回声心脏图片数据集上训练基于视频的深度学习算法 (EchoNet-Dynamic).
- 评估了该模型在左心室细分,喷射分数预测和心力衰竭分类方面的表现.
- 对来自不同医疗保健系统的外部数据集进行了验证,并对人类测量进行了前性评估.
主要成果:
- 在左心室细分 (迪斯相似系数为0. 92) 和射出分数预测 (平均绝对误差为4. 1%) 中,EchoNet-Dynamic实现了高精度.
- 该模型可靠地对心力衰竭进行了分类,其射出分数减少 (AUC为0. 97) 并在外部数据上表现出强的表现 (AUC为0. 96).
- 预期评估显示模型的差异与人类专家相比或小,可复制性得到改善.
结论:
- 在关键心声图分析任务中,EchoNet-Dynamic的性能超过了人类专家.
- 这种算法为准确的心血管疾病诊断提供了更可复制和潜在的实时方法.
- 该研究提供了公开可用的数据集,以促进心脏功能评估的进一步研究和创新.
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