深度学习用于检测胸部X射线中的左心室结构异常
Shreyas Bhave1, Victor Rodriguez1, Timothy Poterucha2
1Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA.
European heart journal
|March 19, 2024
概括
深度学习模型可以准确地检测心脏异常,如左心室缩和扩张从胸部X射线,帮助早期心力衰竭的识别. 这项研究为未来的创新提供了有价值的数据集.
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:
- 早期发现心脏结构异常对于治疗心力衰竭至关重要.
- 胸部X射线 (CXR) 是常见的诊断工具,具有可扩展查的潜力.
- 深度学习 (DL) 可以分析CXR,寻找B期心力衰竭或更严重的症状.
研究的目的:
- 使用CXRs开发一个DL模型来识别严重的左心室缩 (SLVH) 和扩张的左心室 (DLV).
- 评估模型在检测这些心脏结构异常方面的表现.
主要方法:
- 利用了来自24,689名患者的71,589个CXR,与心声图数据联系在一起.
- 提取了SLVH,DLV的标签,以及心声回声图的复合结果.
- 经过DL模型的培训和验证,通过AUROC和外部验证评估性能.
主要成果:
- 在DL模型中,SLVH的AUROC为0.79,DLV为0.80,复合结果为0.80.
- 在外部数据集上表现一致.
- 该模型超越了个别放射科医生,并且表现出比放射科医生共识更高的灵敏度.
结论:
- 对CXR的DL分析对于检测LV缩和扩张是有效的.
- 这种方法对早期识别患有这些心脏病的患者有希望.
- 一个公开的数据集71589CXRs与心声学标签现在可用于促进研究.
相关概念视频
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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