Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials

Javier Escudero, John P Zajicek, Emmanuel Ifeachor, Alzheimer’s Disease Neuroimaging Initiative

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Pages7957-60
Number of pages4
Volume2011
ISBN (Electronic)978-1-4244-4122-8
ISBN (Print)978-1-4244-4121-1
DOIs
Publication statusPublished - 2011

Keywords

  • Aged
  • Alzheimer Disease
  • Artificial Intelligence
  • Clinical Trials as Topic
  • Demography
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Mild Cognitive Impairment
  • Sample Size

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