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Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly.

Yoon Seong Choi1,2, Pei Ing Ngam3, Jeong Ryong Lee4

  • 1Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore.

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|October 8, 2025
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Summary

Deep learning harmonization of amyloid PET improves prediction of cognitive decline in non-demented individuals. This novel approach enhances prognostic accuracy, suggesting it can complement existing amyloid PET measures for early Alzheimer's disease detection.

Keywords:
Alzheimer’s diseaseamyloid PETcognitive declinecognitive impairmentdeep learningdementiaprognosis

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Gerontology

Background:

  • Conventional amyloid PET harmonization across tracers lacks robustness.
  • Accurate prediction of cognitive decline is crucial for early Alzheimer's disease (AD) intervention.

Purpose of the Study:

  • To evaluate deep learning-based harmonization of amyloid PET in predicting conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and MCI to AD.
  • To assess the prognostic performance of a deep learning-derived amyloid PET probability score (DL-ADprob).

Main Methods:

  • Developed a deep learning model to classify AD-dementia vs CU across multiple amyloid PET tracers using data from ADNI, Japanese ADNI, and AIBL cohorts (n=1050).
  • Evaluated DL-ADprob's prognostic value in predicting cognitive decline in ADNI-MCI (n=451) and HABS-CU (n=271) participants over 4 years.
  • Calculated intraclass correlation coefficients (ICCs) for DL-ADprob across tracers in the GAIN dataset (n=155).

Main Results:

  • DL-ADprob was independently prognostic in both ADNI-MCI (P<.001) and HABS-CU (P=.048) cohorts.
  • Incorporating DL-ADprob improved prognostic performance in both ADNI-MCI (tdAUC 0.758 to 0.782) and HABS-CU (tdAUC 0.846 to 0.870) groups.
  • DL-ADprob demonstrated high consistency across tracers (ICCs 0.913-0.935).

Conclusions:

  • Deep learning-based harmonization of amyloid PET significantly enhances the prediction of cognitive decline in non-demented individuals.
  • DL-ADprob shows potential as a complementary tool to conventional amyloid PET measures for improved prognostic accuracy in early AD detection.