Table 5.

Comparison with other studies that use multimodal data.

Study	Method	Data source	Brain region	Objective	Performance
This study	3D-CNN multimodal	Baseline MRI +	Whole brain	Predicting fast decliners	AUC: 0.70
		Baseline MMSE +			Acc: 63.3%
		Demographic data +			Sens: 60.8%
		Baseline scalar volume			Spec: 65.2%
Lee et al.4	Recurrent NN multimodal	Baseline MRI +	Regional hippocampus	MCI-to-AD conversion	AUC: 0.86
		Demographic data +			Acc: 81%
		Long. CSF biomarkers +			Sens: 84%
		Long. cognitive performance +			Spec: 80%
Lin et al.28	2.5D-CNN multimodal PCA + Lasso + NN	Baseline MRI +	Regional hippocampus	MCI-to-AD conversion	AUC: 0.86
		325 free surfer feature			Acc: 79.9%
					Sens:84.0%
					Spec:74.8%
Lu et al.37	Multimodal multiscale NN	MRI + FDG-PET	Whole brain	Stable MCI versus progressive MCI	Acc: 82.9%
		Long. time-points			Sens:79.7%
					Spec:83.8%
Esmaeilzadeh et al.14	Multimodal	MRI +	Whole brain	AD-NC-MCI classification	Acc: 94.1%
		Age + Gender			Sens:94%
					Sens:91%
Moradi et al.8	Random forest multimodal	Baseline MRI + Age	Whole brain	MCI-to-AD conversion	AUC: 0.9
		Baseline cognitive measurements +			Acc: 82%
		(RAVLT + ADAS-cog)			Sens: 87%
		(MMSE + CRD-SB + FAQ)			Spec:74%
Spasov et al.35	3D-CNN multimodal	MRI +	Whole brain	MCI-to-AD conversion	AUC: 0.925
		Demographic data +			Acc: 86%
		Neuropsychological data +			Sens: 87.5%
		APOe4 genetic data			Spec:85%

ML, machine learning; 2-D: two-dimension, 3-D: three-dimension; NN, neural network; CNN, convolutional neural network; R-CNN, recurrent convolutional neural network; Acc, accuracy; Sens, sensitivity; Spec, specificity; AUC, area under curve; CSF, cerebrospinal fluid; Long, longitudinal; RAVLT, Rey’s auditory verbal learning test; ADAS-cog, Alzheimer’s disease assessment scale cognitive subtest; MMSE, mini-mental state examination; CDR-SB, clinical dementia rating sum of boxes; FAQ, functional activities questionnaire.