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Table 2: Linear mixed-effects model analysis of different methods for classifying amyloid PET imaging
Factor Accuracy Sensitivity Specificity
Odds Ratio P Value Odds Ratio P Value Odds Ratio P Value
Type (ResNet-50 vs. -152) 0.96 (0.89–1.03) .258 0.916 (0.82–1.02) .112 1.00 (0.89-1.11) .944
Method (binary classification vs. regression first) 0.79 (0.70–0.89) <.001 0.54 (0.45–0.63) <.001 1.24 (1.03–1.50) .024
Initialization (random vs. pretrained) 0.33 (0.30–0.37) <.001 0.45 (0.38–0.53) <.001 0.22 (0.19–0.26) <.001
Slices (1 vs. 3) 1.17 (1.08–1.26) <.001 1.15 (1.03-1.28) .012 1.22 (1.10–1.36) <.001
Parenthesis refer to 95% confidence intervals for odds ratios.

Factor	Accuracy		Sensitivity		Specificity
Factor	Odds Ratio	P Value	Odds Ratio	P Value	Odds Ratio	P Value
Type (ResNet-50 vs. -152)	0.96 (0.89–1.03)	.258	0.916 (0.82–1.02)	.112	1.00 (0.89-1.11)	.944
Method (binary classification vs. regression first)	0.79 (0.70–0.89)	<.001	0.54 (0.45–0.63)	<.001	1.24 (1.03–1.50)	.024
Initialization (random vs. pretrained)	0.33 (0.30–0.37)	<.001	0.45 (0.38–0.53)	<.001	0.22 (0.19–0.26)	<.001
Slices (1 vs. 3)	1.17 (1.08–1.26)	<.001	1.15 (1.03-1.28)	.012	1.22 (1.10–1.36)	<.001