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Computer assisted diagnosis of Alzheimer’s disease using statistical likelihood-ratio test

Author

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  • Xiaoming Zheng
  • Justin Cawood
  • Chris Hayre
  • Shaoyu Wang
  • for the Alzheimer’s Disease Neuroimaging Initiative Group

Abstract

The purpose of this work is to present a computer assisted diagnostic tool for radiologists in their diagnosis of Alzheimer’s disease. A statistical likelihood-ratio procedure from signal detection theory was implemented in the detection of Alzheimer’s disease. The probability density functions of the likelihood ratio were constructed by using medial temporal lobe (MTL) volumes of patients with Alzheimer’s disease (AD) and normal controls (NC). The volumes of MTL as well as other anatomical regions of the brains were calculated by the FreeSurfer software using T1 weighted MRI images. The MRI images of AD and NC were downloaded from the database of Alzheimer’s disease neuroimaging initiative (ADNI). A separate dataset of minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) was used for diagnostic testing. A sensitivity of 89.1% and specificity of 87.0% were achieved for the MIRIAD dataset which are better than the 85% sensitivity and specificity achieved by the best radiologists without input of other patient information.

Suggested Citation

  • Xiaoming Zheng & Justin Cawood & Chris Hayre & Shaoyu Wang & for the Alzheimer’s Disease Neuroimaging Initiative Group, 2023. "Computer assisted diagnosis of Alzheimer’s disease using statistical likelihood-ratio test," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0279574
    DOI: 10.1371/journal.pone.0279574
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