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10.1371/journal.pone.0173372 http://scihub22266oqcxt.onion/10.1371/journal.pone.0173372 C5338815!5338815!28264071     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (3): ä Nephropedia Template TP {{tp|p=28264071|t=2017. Prediction and classification of Alzheimer disease based on quantification of MRI deformation |pdf=|usr=}}{{28264071}} gab.com Text Prediction and classification of Alzheimer disease based on quantification of MRI deformation JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28264071 #FOAvip Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer?s disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination. Twit Text FOAVip Prediction and classification of Alzheimer disease based on quantification of MRI deformation ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28264071 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28264071 #FOAvip English Wikipedia <ref>{{Cite pmid|28264071}}</ref> Prediction and classification of Alzheimer disease based on quantification of MRI deformation #MMPMID28264071 Long X; Chen L; Jiang C; Zhang L PLoS One 2017[]; 12 (3): ä PMID28264071show ga Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer?s disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination. äPrediction and classification of Alzheimer disease based on quantifica(epmc).pdf DeepDyve Pubget Overpricing