End to end stroke triage using cerebrovascular morphology and machine learning.
End to end stroke triage using cerebrovascular morphology and machine learning.
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DOI：
10.3389/fneur.2023.1217796
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发表时间：
2023
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
--
作者：
急性缺血性卒中（AIS）的快速准确分诊对于早期血运重建和改善患者结局至关重要。对急性再灌注治疗的反应根据控制脑血流的患者特定脑血管解剖结构而显著不同。我们提出了一种端到端的机器学习方法，用于自动中风分诊。采用经过验证的卷积神经网络（CNN）分割模型进行图像处理，我们从基线非侵入性血管造影扫描中提取每个患者的血管及其形态特征。这些特征用于自动检测阻塞的存在和位置，并首次在没有手动干预的情况下估计侧支循环。然后，我们使用提取的脑血管功能沿着与常用的临床和成像参数，以预测90天的功能结果为每个病人。CNN模型基于Dice相似系数（DSC）实现了94%的分割准确率。自动中风检测算法的灵敏度和特异性分别为92%和94%。闭塞部位检测和自动侧支分级模型的准确率分别达到96%和87.2%。自动提取的脑血管特征显著提高了90天预后预测准确率，从0.63提高到0.83。本文提出的快速、自动和全面的模型可以利用脑血管形态学改善卒中诊断，辅助侧支评估，并增强治疗决策的解释性。
Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage. Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient’s cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion’s presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient. The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83. The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.
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影响因子：
3.3
作者：
Hsu CY;Schneller B;Alaraj A;Flannery M;Zhou XJ;Linninger A
通讯作者：
Linninger A
DOI：
10.1111/jon.13023
发表时间：
2022-09
期刊：
Journal of neuroimaging : official journal of the American Society of Neuroimaging
影响因子：
--
作者：
通讯作者：
--
影响因子：
4.2
作者：
Bullitt, Elizabeth;Zeng, Donglin;Mortamet, Benedicte;Ghosh, Arpita;Aylward, Stephen R.;Lin, Weili;Marks, Bonita L.;Smith, Keith
通讯作者：
Smith, Keith
DOI：
10.29220/csam.2019.26.6.591
发表时间：
2019-11-01
影响因子：
0.4
作者：
Lee, Hagyeong;Song, Jongwoo
通讯作者：
Song, Jongwoo
影响因子：
6.3
作者：
Kemmling, Andre;Flottmann, Fabian;Fiehler, Jens
通讯作者：
Fiehler, Jens