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Title of Journal: Int J CARS

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Abbravation: International Journal of Computer Assisted Radiology and Surgery

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Springer-Verlag

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DOI

10.1007/978-3-642-12909-4

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1861-6429

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Automated MR morphometry to predict Alzheimer’s di

Authors: Klaus H Fritzsche Bram Stieltjes Sarah Schlindwein Thomas van Bruggen Marco Essig HansPeter Meinzer
Publish Date: 2010/05/04
Volume: 5, Issue: 6, Pages: 623-632
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Abstract

Prediction of progression from mild cognitive impairment MCI to Alzheimer’s disease AD is challenging but essential for early treatment This study aims to investigate the use of hippocampal atrophy markers for the automatic detection of MCI converters and to compare the predictive value to manually obtained hippocampal volume and temporal horn widthA study was performed with 15 patients with Alzheimer and 18 patients with MCI ten converted eight remained stable in a 3year followup as well as 15 healthy subjects MRI scans were obtained at baseline and evaluated with an automated system for scoring of hippocampal atrophy The predictive value of the automated system was compared with manual measurements of hippocampal volume and temporal horn width in the same subjectsThe conversion to AD was correctly predicted in 778 of the cases sensitivity 70 specificity 875 in the MCI group using automated morphometry and a plain linear classifier that was trained on the AD and healthy groups Classification was improved by limiting analysis to the left cerebral hemisphere accuracy 833 sensitivity 70 specificity 100 The manual linear and volumetric approaches reached rates of 667 40/100 and 722 60/875 respectivelyThe automatic approach fulfills many important preconditions for clinical application Contrary to the manual approaches it is not observerdependent and reduces human resource requirements Automated assessment may be useful for individual patient assessment and for predicting progression to dementia


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