The early-stage startup, whose products are already a breakout hit in Asia and the Middle East, has more than 50 job openings, including at least 40 roles in the United States.ĭUBAI, UAE, Sept. Asia's leading food tech startup, Next Gen Foods, is rolling out its flagship product - TiNDLE plant-based chicken - with top chefs in key regions globally. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.( MENAFN- PR Newswire) Konrad, who spent five years as Chief Communications Officer at Impossible Foods, joins the board of Singapore-based Next Gen Foods during a period of international hypergrowth. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories.ĭeep learning epilepsy neuroimaging pediatric stereoelectroencephalography. We have created a framework for incorporation of deep-learning-based MRI lesion detection into sEEG implantation planning. There was a high degree of colocalization between automated lesion detection and sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted.

In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. The algorithm correctly detected seven of eight of these FCDs (86%).

Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. No clusters were detected in the controls (specificity = 100%). In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. (2) Specificity was assessed in 20 pediatric healthy controls. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG.Ī neural network classifier was applied to cortical features from MRI data from three cohorts. This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy.

Rachel konrad