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Skip to 0 minutes and 14 seconds Evaluation of nidus, brain tissue, and CSF segmentation. With the use of linear regression and the brain Altman plot to evaluate. The red lines and dots regression. The red lines and dots represent the Nidus, Brain Tissue. The nidus. the green lines and dots represent brain tissue; and the blue lines and dots represent CSF. Radiation exposure region, manual segmentation, and automated segmentation we can see in the figure. In T2w images. The red curve indicates the boundary of the prescription isodose region. The blue, green, and red colors respectively indicate the nidus, brain tissue, and CSF.

Skip to 1 minute and 15 seconds We also used SI to determine the degree to which the proposed automated segmentation method agrees with manual clustering in terms of sensitivity, specificity, and average performance for the three types of tissue. The proposed algorithm outperformed manual clustering

Skip to 1 minute and 43 seconds and specificity in detecting the nidus: SI equals 74%; Sensitivity is 83%, and specificity is 86%. The results for brain tissue were as follows. The results of CSF and Nidus. The SI value over 0.7 is regarded as strong agreement. And further, we correlated the results obtained using the imaging algorithm and clinical follow-up results from the 234 AVM patients with unruptured AVM who underwent upfront before the GKRS radiosurgery. This includes 98 females and 136 man patients. The median volume of the prescription isodose region was 21 ml (range from 7 to 53 ml). The median prescription peripheral dose for the nidus was 17Gy (range from 15 to 18.5Gy).

Skip to 3 minutes and 12 seconds This figure shows that Box plot showing the correlation between ARE and the brain tissue component of cerebral AVM. Cases with a greater number of brain tissue components are prone to ARE following radiosurgery.

Method for Evaluating the Risks of Radiotherapy

Prof. Peng continues to explain the evaluation of the nidus, brain tissue, and CSF segmentation results. Next, he will explain the performance of the computer-assisted classification method.

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Applications of AI Technology

Taipei Medical University