用于通过CT检测创伤性颅内出血的人工智能:面向工作流程的实现

Artificial intelligence for detecting traumatic intracranial haemorrhage with CT: A workflow-oriented implementation.

作者信息

Abed Selim, Hergan Klaus, Pfaff Johannes, Dörrenberg Jan, Brandstetter Lucas, Gradl Johann

机构信息

Department of Radiology, University Hospital Salzburg, Paracelsus Medical University, Austria.

Department of Neuroradiology, University Hospital Salzburg, Paracelsus Medical University, Austria.

出版信息

Neuroradiol J. 2025 Jun 3:19714009251346477. doi: 10.1177/19714009251346477.

Abstract

The objective of this study was to assess the performance of an artificial intelligence (AI) algorithm in detecting intracranial haemorrhages (ICHs) on non-contrast CT scans (NCCT). Another objective was to gauge the department's acceptance of said algorithm. Surveys conducted at three and nine months post-implementation revealed an increase in radiologists' acceptance of the AI tool with an increasing performance. However, a significant portion still preferred an additional physician given comparable cost. Our findings emphasize the importance of careful software implementation into a robust IT architecture.

摘要

本研究的目的是评估一种人工智能(AI)算法在非增强CT扫描(NCCT)上检测颅内出血(ICH)的性能。另一个目的是衡量该科室对所述算法的接受程度。在实施后的三个月和九个月进行的调查显示,随着性能的提高,放射科医生对AI工具的接受度有所增加。然而,在成本相当的情况下,仍有很大一部分人更喜欢有另一位医生参与。我们的研究结果强调了在强大的IT架构中谨慎实施软件的重要性。

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