Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke

Background: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to...

Bibliografiset tiedot
Päätekijät:	Chun-Chao Huang, Hsin-Fan Chiang, Cheng-Chih Hsieh, Chao-Liang Chou, Zong-Yi Jhou, Ting-Yi Hou, Jin-Siang Shaw
Aineistotyyppi:	Artikkeli
Kieli:	English
Julkaistu:	MDPI AG 2023-03-01
Sarja:	Tomography
Aiheet:	multiphase CTA collateral status artificial intelligence convolutional neural network acute ischemic stroke
Linkit:	https://www.mdpi.com/2379-139X/9/2/52

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