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Artificial Intelligence Image

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Hello, ladies and gentlemen. My name is Syu-Jyun Peng. I come from Taipei Medical University It’s my honor to introduce the topic. The title of the topic is Artificial Intelligence Image Analytics of Lesional and Non-lesional Neurological Disorders with magnetic resonance imaging (MRI) including its chemical stroke, cerebral AVM and Non-lesional epilepsy. This is my outline, including introduction, method and results, and conclusions. Magnetic Resonance Image (MRI) contains pathology-related information. Detection of MRI-based biomarkers is of diagnostic and therapeutic value. Accurate detection of this type of markers is challenging because they may not be directly discernible and some are even non-lesional on conventional MRI. Any characteristic of cerebral tissue can be objectively evaluated and represented a parameter of its biological, functional or structural organization.
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An imaging biomarker is a measurable parameter obtained with standard and advanced techniques to explore, quantify and represent a tissue specific property. These properties are extracted after applying to the acquired images different computational models and specific statistical processing. The parametric maps illustrate the spatial distribution and signal intensity in the analyzed tissue. Medical imaging is an important technique in diagnosis, treatment monitoring and prognosis of the therapeutic response of diseases. It is also a fundamental means for guiding many minimally invasive therapeutic procedures. Traditional radiological diagnosis is based on the integration and qualitative assessment of imaging findings obtained from conventional radiography, ultrasound, computed tomography and magnetic resonance imaging (MRI). Technology and engineering have improved to acquire a variety of information from medical imaging.
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The knowledge of pathological and physiological information of neurological diseases has also elevated the application of these new parameters, known as biomarkers. This dissertation presents different methods for finding lesional biomarkers of acute ischemic stroke and non-lesional ones of neocortical seizures. For lesional biomarkers of acute ischemic stroke, we proposed a computer-assisted segmentation and quantification method to depict cerebral infarct, white matter hyperintensities (WMH), and AVM. For non-lesional biomarkers of neocortical epilepsy, a fiber-labeled MRI template was transformed to each subject’s neuroanatomy to generate personalized atlases for objective and automated regional-of-interest demarcation.

In this video, Prof. Peng will introduce Artificial Intelligence Image Analytics of Lesional and Non-lesional Neurological Disorders with Magnetic Resonance Imaging.

He will first introduce Magnetic Resonance Image (MRI). Magnetic Resonance Imaging (MRI) is a non-invasive imaging technology that produces three dimensional detailed anatomical images. It is often used for disease detection, diagnosis, and treatment monitoring. Prof. Peng will introduce MRI pathology-related information, including diagnosis, treatment monitoring and prediction of therapeutic response. Medical imaging is an important technique in diagnosis, treatment monitoring and prognosis of the therapeutic response of diseases.

Technology and engineering have improved to acquire a variety of information from medical imaging. The knowledge of pathological and physiological information of neurological diseases has also elevated the application of these new parameters, known as biomarkers. He will also explain the difference between lesional biomarkers and non-lesional biomarkers.

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Applications of AI Technology
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