基于MRI的新型深度学习放射学框架用于评估中枢神经系统感染中的脑脊液信号
Ferhat Cüce1, Gökalp Tulum2, Muhammed Ikbal Isik1
1Department of Radiology, Health Science University, Gulhane Training, and Research Hospital, Ankara, Türkiye.
Frontiers in medicine
|September 5, 2025
概括
这项研究引入了一种使用深度学习和放射学来非侵入性检测中枢神经系统感染的新型MRI框架. 这种方法对诊断脑脊液变化具有前景,有可能取代侵袭性腰部穿刺.
科学领域:
- 医学成像
- 医学的人工智能
- 神经学
背景情况:
- 准确诊断中枢神经系统感染 (CNSIs) 是至关重要的,但目前的方法如腰部穿刺 (LP) 是侵入性的和缓慢的.
- 需要新的非侵入性诊断工具来改善患者的治疗结果和简化临床工作流程.
研究的目的:
- 开发和验证使用磁共振成像 (MRI),放射学和深度学习 (DL) 来识别急性中枢神经系统损伤中的脑脊液 (CSF) 改变的非侵入性框架.
- 将混合DL-放射学模型的性能与CNSI分类的基本DL架构进行比较.
主要方法:
- 对52名急性CNSI患者和52名对照患者的回顾性分析,所有患者在入院后48小时内接受了MRI.
- 在T2权重的MRI中,从心室和子晶状细胞核 (包括周血管空间) 来分离与脑脊髓液相关的信号.
- 开发混合型的DenseASPP-RadFusion和MobileASPP-RadFusion模型,将放射学和DL功能融合在一起,与DenseNet-201和MobileNet-V3Large进行比较,使用5倍交叉验证.
主要成果:
- 混合型的DenseASPP-RadFusion模型在子晶状核膜中取得了最高的分类性能 (准确率:78.57%).
- 来自微量过的MRI数据的放射特征显示出优异的辨别能力.
- 亚晶状核区域被证明是最可靠的解剖目标,用于区分感染和非感染的脑脊液.
结论:
- 已经建立了一个基于MRI的强大且可解释的深度学习放射学管道,用于CNSI分类.
- 该框架提供了一个潜在的非侵入性替代LP,特别是通过分析parenchymal区域的CSF信号.
- 这些发现为这种新型诊断方法的多中心验证和临床整合提供了基础.
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