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Skip to 0 minutes and 14 secondsFirst, part one, I will introduce Lesional Segmentation in Acute Ischemic Stroke Patients Cerebrovascular disease is one of the leading causes of acute mortality and chronic disability. The volume of infarct and white matter hyperintensities is associated with severity of acute ischemic stroke and correlates with clinical prognosis and the effect of endovascular therapy. A rapid and reliable method of determination of volume of acute infarct will help predict the prognosis This slide is showing Subjects and Image Acquisition. The diffusion weighted imaging (DWI) is more sensitive than other magnetic resonance imaging (MRI) modalities to small water diffusion changes in the acute ischemic brain, especially within 48 hours of the infracts.

Skip to 1 minute and 40 secondsWe aimed to design a DWI-based computer-assisted method to provide doctor a prompt and accurate determination of the volumes of acute cerebral infarct. The operation of this method is based on the histographic characteristic of the output clusters of a fuzzy C-means (FCM) clustering. Additional measures were taken to ensure the accuracy of infarct detection, including discriminating infarcts from artifacts due to magnetic inhomogeneity by incorporating the histographic information in the apparent diffusion coefficient (ADC) map. It's meaning ADC map. Landseed Hospital has been participating in the nationwide Taiwan Stroke Registry, which prospectively registered patients with stroke onset within 10 days. For this study, we recruited 22 patients with acute cerebral infarction and MRI examinations.

Lesional Segmentation

Prof. Peng will introduce Lesional Segmentation in Acute Ischemic Stroke Patients. As Cerebrovascular disease is one of the leading causes of acute mortality and chronic disability, Prof. Peng explains a designed DWI-based computer-assisted method to tackle this type of patient. If you want to learn more on the difference of DWI and MRI, here is a reference for you to see, The Definition of DWI and MRI.

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Applications of AI Technology

Taipei Medical University