人工智能工具用于多步骤急性中风成像的性能:一个多中心诊断研究
Thibault Agripnidis1, Angela Ayobi2, Sarah Quenet2
1APHM, Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital de la Timone, Marseille 13005, France.
European journal of radiology open
|September 11, 2025
概括
这项研究表明,人工智能工具可靠地检测中风成像中的脑内出血 (ICH) 和大血管封闭 (LVO). 人工智能工具还准确计算阿尔伯塔中风计划早期CT分数 (ASPECTS),帮助更快地进行中风诊断.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在全球范围内,中风仍然是导致死亡和残疾的主要原因.
- 目前的中风成像工作流程可能耗时,延迟关键的治疗决策.
- 人工智能 (AI) 为提高中风诊断效率提供了潜在的解决方案.
研究的目的:
- 评估基于人工智能的中风成像设备的综合性能.
- 评估人工智能工具在检测脑内出血 (ICH) 和大血管封闭 (LVO) 的准确性.
- 确定人工智能工具在计算阿尔伯塔中风计划早期CT分数 (ASPECTS) 的能力.
主要方法:
- 从中风患者的无对比CT (NCCT) 和计算机断层扫描血管造影 (CTA) 扫描的回顾性收集.
- 使用CINA-HEAD AI工具 (Avicenna.AI) 处理成像数据,用于ICH,LVO检测和ASPECTS计算.
- 将人工智能结果与四位专家神经放射学家的实地真相评估进行比较.
主要成果:
- 人工智能工具在NCCT上实现了94.6%的ICH检测准确度.
- 在CTA上的LVO识别记录了86.4%的准确性.
- 基于区域的ASPECTS分析的准确率为88.6%,而二分化ASPECTS分类 (≥6) 的准确率为80.4%.
结论:
- 基于人工智能的中风成像工具展示了可靠的,逐步的性能.
- 人工智能工具有效地协助对中风的诊断级联.
- 这项技术显示了简化急性中风分拣和决策流程的潜力.
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