自动阿尔伯塔中风计划在急性缺血性中风患者中使用扩散加权成像的早期计算机断层扫描评分

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Yan Wu¹, Rong Sun¹, Yuanzhong Xie²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China.

Medical & biological engineering & computing

|June 22, 2023

概括

深度学习准确地为急性中风提供扩散权重成像阿尔伯塔中风计划早期计算机断层扫描 (DWI-ASPECTS) 评分,帮助快速做出治疗决策. 新的CBAM-VGG模型提高了小大脑区域的准确性.

科学领域:

神经成像是一种神经成像.
人工智能在医学中的应用
脑卒中成像脑卒中成像

背景情况:

阿尔伯塔中风计划早期计算机断层扫描评分 (ASPECTS) 对于评估急性缺血性中风的早期缺血性变化至关重要.
准确的ASPECTS评分对于及时的临床决策至关重要,特别是对于内血管血栓切除术.
手动的ASPECTS评估可能耗时,并且受观察者之间的变化影响.

研究的目的:

为扩散权重成像方面 (DWI-ASPECTS) 开发和验证基于深度学习的自动评估策略.
提高DWI-ASPECTS评分的准确性,特别是在体积小的地区.
为临床医生提供可靠的工具,以协助急性缺血性中风的紧急决策.

主要方法:

从DWI系列中提取十个ASPECTS区域,用于独立分类网络培训.
古典卷积神经网络 (VGG-16,ResNet-50) 的验证.
开发一种新的CBAM-VGG模型,以提高四个小体积ASPECTS区域的评分准确性 (尾状核,晶状核,内部囊,岛叶).

主要成果:

在六个皮质区域 (M1-M6) 获得了0.929的F1平均得分,在四个小ASPECTS区域获得了0.840的F1平均得分.
在皮质区域获得了94.75%的平均准确度,在小型ASPECTS区域获得了84.99%的平均准确度.
CBAM-VGG模型在估计四个规模较小的ASPECTS区域的准确性方面取得了显著的改进.

结论:

深度学习方法提高了自动DWI-ASPECTS评分的效率和稳定性.
提出的自动评分策略,特别是CBAM-VGG,为急性缺血性中风的临床决策提供了宝贵的参考.
自动化DWI-ASPECTS评分可以支持更快,更一致的内血管血栓切除术治疗计划.

关键词:

急性缺血性中风是一次急性缺血性中风.阿尔伯塔州中风计划早期计算机断层扫描评分卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.扩散加权成像技术的使用.

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