biostudies-literatureWang YChina Postdoctoral Science FoundationNational Natural Science Foundation of China755492https://www.ebi.ac.uk/biostudies/studies/S-EPMC8961979biostudies-literatureUnknown13<h4>Background</h4>Computed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up.<h4>Methods</h4>We deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculFrontiers in neurologyDeep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.PMC8961979819731452019M651805Fan XJiang LLi CLu XHu WMeng XLi MDou SZhao XLi WYe MZhu JZhang XHao HWang YZhao JHu RChen TWu XYan FfalseDeep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.<h4>Background</h4>Computed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up.<h4>Methods</h4>We deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calcul2022-01-01T00:00:00Z20222025-04-04T23:34:54.125Z2025-02-19T01:23:41.581ZS-EPMC89619793535962610.3389/fneur.2022.755492