Document Detail


Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials.
MedLine Citation:
PMID:  22256186     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
There is a need for objective tools to help clinicians to diagnose Alzheimer's Disease (AD) early and accurately and to conduct Clinical Trials (CTs) with fewer patients. Magnetic Resonance Imaging (MRI) is a promising AD biomarker but no single MRI feature is optimal for all disease stages. Machine Learning classification can address these challenges. In this study, we have investigated the classification of MRI features from AD, Mild Cognitive Impairment (MCI), and control subjects from ADNI with four techniques. The highest accuracy rates for the classification of controls against ADs and MCIs were 89.2% and 72.7%, respectively. Moreover, we used the classifiers to select AD and MCI subjects who are most likely to decline for inclusion in hypothetical CTs. Using the hippocampal volume as an outcome measure, we found that the required group sizes for the CTs were reduced from 197 to 117 AD patients and from 366 to 215 MCI subjects.
Authors:
Javier Escudero; John P Zajicek; Emmanuel Ifeachor
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2011     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2012-01-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  7957-60     Citation Subset:  IM    
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