Skip to main content

Table 2 Classification performance for Aβ PET positivity on ADNI, KBASE, and Severance hospital datasets

From: Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET

  Mean
(95% CI)
AUC value Accuracy Sensitivity Specificity F1-score
Internal validation (ADNI and KBASE) All 0.811
(0.803, 0.819)
0.733
(0.726, 0.740)
0.678
(0.664, 0.691)
0.785
(0.771, 0.799)
0.709
(0.701, 0.717)
CU 0.717
(0.705, 0.729)
0.748
(0.737, 0.758)
0.381
(0.352, 0.410)
0.870
(0.857, 0.883)
0.422
(0.398, 0.446)
MCI 0.757
(0.746, 0.768)
0.682
(0.672, 0.692)
0.651
(0.636, 0.666)
0.718
(0.698, 0.738)
0.690
(0.680, 0.701)
AD 0.816
(0.789, 0.843)
0.862
(0.848, 0.875)
0.950
(0.939, 0.960)
0.336
(0.283, 0.390)
0.921
(0.912, 0.929)
External validation (Severance hospital) All 0.798
(0.789, 0.807)
0.690
(0.681, 0.699)
0.768
(0.748, 0.789)
0.612
(0.586, 0.639)
0.712
(0.703, 0.721)
MCI 0.769
(0.762, 0.776)
0.698
(0.691, 0.705)
0.702
(0.688, 0.715)
0.694
(0.675, 0.714)
0.718
(0.711, 0.725)
Demented participants 0.806
(0.803, 0.809)
0.599
(0.573, 0.624)
0.868
(0.850, 0.886)
0.462
(0.414, 0.509)
0.598
(0.587, 0.609)
  1. Aβ = β-amyloid; CI = Confidence interval; AUC = Area under curve