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Skip to 0 minutes and 14 secondsFinally, we will conclude the topic. The software using proposed computer-assisted prompt evaluation technique can precisely calculate the actual percentage of different tissue components included in the nidus of cerebral AVM which we delineated in T2w imaging in Gamma plan. Higher the percentage of intervening brain parenchyma in the nidus of cerebral AVM is related to higher rate of adverse radiation effect after Gamma knife Radiosurgery. And about the lesional biomarkers of acute ischemic stroke, we proposed a computer-assisted segmentation and quantification method to depict cerebral infarct and white matter hyperintensities (WMH). The cerebral infarct and WMH volume were measured based on the histographic distribution of lesions to define self-adjusted intensity thresholds value using multispectral MRI.

Skip to 1 minute and 55 secondsThe proposed method attained high agreement with the semi-automatic method. For non-lesional biomarkers of neocortical epilepsy, a popular fiber-labeled MRI template was transformed to each subject’s neuroanatomy to generate personalized atlases for objective and automatic region-of-interest (ROI) demarcation. We investigated supratentorial white matter and subcortical gray matter structures from high-resolution raw structural images and diffusion tensor images with automatic ROI registrations in neocortical seizures. The automatic methods presented in this dissertation facilitates the exploration of lesional and non-lesional biomarkers of neurological disorders for assisting the clinical diagnosis, identifying the risk, and helping guide the treatment and prognosis of the diseases. Thank you for your attention.

Conclusions

Prof. Peng will conclude the research result here. What’s the finding of the MRI analysis? Although the method of detecting acute ischemic stroke is complicated, the automatic methods did show how AI can assist clinical diagnosis, identifying the risk of the patients, and help to guide the treatments and prognosis of the diseases.

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Applications of AI Technology

Taipei Medical University