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自动化内血块检测:在非增强型CT中检测血管阻塞的一个有希望的工具

Ricarda Schwarz¹, Georg Bier^2,3, Vera Wilke^4,5

¹Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany.

Diagnostics (Basel, Switzerland)

|September 28, 2023

概括

一个新的AI软件原型显示了在非增强型CT扫描上检测内动脉凝块的前景,在初步测试中表现优于人类读者. 这种工具可以帮助更快地诊断急性缺血性中风,特别是当CT血管图无法使用时.

科学领域:

神经学神经学
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

评估基于全卷积神经网络 (CNN) 的新型软件原型的诊断性能.
专注于使用非增强型计算机断层扫描 (NECT) 成像数据检测内动脉中的血块.

研究的目的:

评估自动凝块检测系统在识别内动脉血栓栓塞的准确性.
将软件的性能与人类专家阅读器和CT血管造影 (CTA) 进行比较.

主要方法:

追溯分析85名中风和内血管封闭的患者.
使用NECT软件原型,自动计算凝块位置,长度和体积.
自动检测率与神经放射学家视觉评估和CTA确认的比较.

主要成果:

自动化软件实现了66%的整体血栓检测率,超过了人类读者 (46%和24%).
在内动脉 (ICA) 和中脑动脉 (MCA) (88-92%) 中观察到的检测率最高.
在凝块长度和治疗选择 (血栓切除术与保守) 之间发现了显著的相关性.

结论:

自动凝块检测原型证明了在NECT扫描中识别内凝块的潜力,特别是在ICA和MCA中.
这种人工智能工具可以帮助放射科医生在紧急情况下,加速急性缺血性中风诊断.
该软件在CTA无法轻松访问的情况下提供了有价值的替代方案.

关键词:

深度学习是一种深度学习.内动脉血管封闭内动脉血管封闭没有增强的大脑CTCT.

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