基于深度学习的自动ASPECTS计算可以提高急性缺血性中风患者的诊断效率:一项多中心研究
Jianyong Wei1,2, Kai Shang3, Xiaoer Wei3
1School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China.
European radiology
|July 26, 2024
概括
一个新的深度学习系统准确且快速地评估阿尔伯塔中风计划急性缺血性中风的早期CT分数 (ASPECTS). 这种自动化工具显著减少了诊断时间,并提高了临床医生的工作流效率.
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:
- 阿尔伯塔中风计划早期CT评分 (ASPECTS) 对于使用非对比计算断层扫描 (NCCT) 评估急性缺血性中风中的缺血性变化至关重要.
- 目前的ASPECTS解释很大程度上依赖于专家的经验,导致读者之间的变化和治疗中的潜在延迟.
- 在临床实践中需要一种标准化,客观和高效的方法来评估ASPECTS.
研究的目的:
- 开发和验证基于深度学习 (DL) 的系统,用于在急性缺血性中风中自动评估ASPECTS.
- 与专家解释相比,评估自动化DL系统的临床适用性和效率.
主要方法:
- 在四个中心的1987年NCCT扫描上进行了DL系统的训练和验证.
- 使用AUC和ICC等指标,对医生共识 (参考标准) 进行了系统性能评估.
- 在现实临床环境中对13399名患者进行了前性验证.
主要成果:
- 在试验队列中,DL系统实现了84.97%的AUC和0.84的ICC.
- 对ASPECTS ≥6的诊断灵敏度为94.61%,与专家有实质性一致 (ICC=0.65).
- 该系统将诊断时间缩短了74.8% (从130.6秒到33.3秒),并在94.0%的潜在病例中使用,96%的医生注意到效率有所提高.
结论:
- 开发的基于DL的系统准确而快速地确定了ASPECTS,可能简化早期中风干预的临床工作流程.
- 该自动化系统证明其不低于专家评级,提高了评估的一致性,并大大缩短了处理时间.
- 虽然有效,但医生验证对于自动化ASPECTS系统的临床应用至关重要.
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