Comparison of Three Automated Approaches for Classification of Amyloid-PET Images

for the Alzheimer‘s Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [11C]PiB and 209 [18F]AV45 PET images from ADNI database and our local cohort were used. Global mean and maximum SUVr cut-points were derived using ROC analysis. 68 ML models were built using regional SUVr values and one DL network was trained with classifications of two visual assessments – manufacturer’s recommendations (gray-scale) and with visually guided reference region scaling (rainbow-scale). ML-based classification achieved similarly high accuracy as ROC classification, but had better convergence between training and unseen data, with a smaller number of training data. Naïve Bayes performed the best overall among the 68 ML algorithms. Classification with maximum SUVr cut-points yielded higher accuracy than with mean SUVr cut-points, particularly for cohorts showing more focal uptake. DL networks can support the classification of definite cases accurately but performed poorly for equivocal cases. Rainbow-scale standardized image intensity scaling and improved inter-rater agreement. Gray-scale detects focal accumulation better, thus classifying more amyloid-positive scans. All three approaches generally achieved higher accuracy when trained with rainbow-scale classification. ML yielded similarly high accuracy as ROC, but with better convergence between training and unseen data, and further work may lead to even more accurate ML methods.

Original languageEnglish
Pages (from-to)1065-1075
Number of pages11
JournalNeuroinformatics
Volume20
Issue number4
DOIs
Publication statusPublished or Issued - Oct 2022
Externally publishedYes

Keywords

  • Alzheimer’s disease
  • Deep Learning
  • Equivocal
  • Machine Learning
  • Positron emission tomography (PET)
  • Visual interpretation

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems

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