使用3D自配置物体检测网络对CTA进行自动化的LVO检测和附带评分:一个多中心研究
Omer Bagcilar1, Deniz Alis2,3, Ceren Alis4
1Radiology Department, Sisli Hamidiye Etfal Research and Training Hospital, Istanbul, Turkey.
Scientific reports
|May 31, 2023
概括
深度学习可以准确地检测大血管封闭 (LVO),并在CT血管造影 (CTA) 扫描上得分抵押. 这种自动化方法有助于更快地诊断LVO患者的中风.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 越来越多地用于自动化医疗图像分析.
- 精确检测大血管封闭 (LVO) 和计算机断层扫描血管学 (CTA) 的附带评分对于中风管理至关重要.
研究的目的:
- 评估一种新型DL模型nnDetection的性能,用于CTA上的自动LVO检测和附带评分.
- 评估模型的准确性和与专家放射科医生的一致性.
主要方法:
- 一个多任务的3D物体检测网络 (nnDetection) 在5个中心的2425个单相CTA扫描上受过训练.
- 该模型的性能在345次CTA扫描的外部测试组中得到验证.
- 三名放射科医生建立了LVO和附带分数的基础真实标签.
主要成果:
- nnDetection模型在识别LVO方面实现了98.26%的诊断准确度,在345次扫描中正确分类了339次.
- 基于DL的附带评分显示与放射科医生的共识有很好的一致性 (kappa = 0.80).
结论:
- 自配置的3D nnDetection模型准确地检测到单相CTA上的LVO.
- 这种DL方法提供了可靠的附带评分,支持LVO患者的自动中风诊断.
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