根据深度学习卷积神经网络检测心脏起器和识别MRI条件的心脏起器,以提高患者安全
Yoonah Do1, Soo Ho Ahn1,2, Sungjun Kim1
1Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Journal of medical systems
|July 31, 2023
概括
一个深层卷积神经网络 (DCNN) 准确地识别胸部X射线上的节拍器,将其分类为MR条件或常规. 这种人工智能工具通过帮助医生验证设备,提高了MRI扫描期间的患者安全.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 增加MRI和心脏装置植入的使用需要谨慎的患者管理.
- 对于常规心脏起器患者来说,MRI通常是禁忌的.
- 条件式心脏起器提供更安全的MRI选择,但需要准确的设备识别.
研究的目的:
- 为了评估一个深层卷积神经网络 (DCNN),用于检测胸部X射线图上的起器.
- 评估DCNN将心脏起器分类为条件或常规的能力.
主要方法:
- 一个DCNN被训练来检测和分类胸部X射线影像中的心脏起器.
- 使用内部和外部测试数据集来验证DCNN的性能.
主要成果:
- DCNN 实现了对节拍器的完美检测.
- 在内部数据集上,分类准确率为100.0%,在外部数据集上为90.1%.
- DCNN在识别设备类型方面表现出了很高的实用性.
结论:
- DCNN是放射学工作流程的一个有价值的工具,有助于心脏起器的识别.
- 这项技术可以通过防止错误来提高MRI检查期间的患者安全.
- 该DCNN支持医生在心脏设备患者的MRI方面做出明智的决定.
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