Journal of the Korean Academy of Child and Adolescent Psychiatry : eISSN 2233-9183 / pISSN 1225-729X

Table. 1.

Table. 1.

Summary of neuroimaging based deep learning studies

Authors (year) Sample size Technique Features DL architecture Comparison Accuracy (%)
Li et al. (2018)23 HC=209 sMRI WB voxel-level Multi-channel HC vs. ASD 76.24
ASD=55 CNN
Li et al. (2019)24 HC=215 sMRI ROI (amygdala, hippocampus) Dilated-Dense HC vs. ASD 79.9 to 92.3
ASD=61 U-Net
Yoo et al. (2019)25 HC=47 sMRI & DTI & rfMRI WB voxel-level & region-level Random forest (RF) HC vs. ADHD 69.4 & 77.8
ADHD=47
Moberget et al. (2019)26 Cohort=1401* sMRI & rfMRI ROI (cerebeilum) ICA
Sidhu et al. (2019)27 HC=124 rfMRI & tfMRI WB voxel-level PCA HC vs. SPR >80
SPR=55 HC vs. ADHD
ADHD=19 HC vs. ASD
ASD=31
Xiao et al. (2019)28 HC=81 rfMRI WB voxel-level SAE HC vs. ASD 96.26
ASD=117
Aghdam et al. (2019)34 ABIDE I & ABIDE II data rfMRI WB voxel-level CNN HC vs. ASD 72.73
Deshpande et al. (2015)35 rfMRI WB voxel-level FCC ANN HC vs. ADHD 90 & 95
ADHD inattentive vs. ADHD combined
Jung et al. (2019)36 HC=125 rfMRI WB voxel-level & regional-level SVM-RFE HC vs. ASD 76.3 to 84.1
ASD=86 HC vs. ADHD
ADHD=83 ASD vs. ADHD
Kuang et al. (2014)38 ADHD-200 consortium data rfMRI ROI (PFC) DBN HC vs. ADHD 44.4 to 80.9
Hao et al. (2015)39 ADHD-200 consortium data rfMRI ROI (PFC, cingulate, somatosensory, visual cortex) Deep bayesian network HC vs. ADHD 48.8 to 72.7
Wang et al. (2018)40 ADHD-200 consortium data rfMRI WB regional-level SVM HC vs. ADHD 78.75
Xu et al. (2020)42 HC=22 fnIRS Temporal variation LSTM, CNN TD vs. ASD 95
ASD=25
Xu et al. (2019)43 HC=22 fnIRS ROI (bilateral inferior frontal gyrus and temporal lobe CGRNN TD vs. ASD 90 to 92.2
ASD=25
Yoo et al. (2019)25 Training dataset=83 DTI ROI (10 frontal lobe structures and two major striatal regions) WEKA Training dataset vs. Independent dataset 90.0 to 95.5
Independent dataset=36

* philadelphia neurodevelopmental cohort, age 8–23 years, ABIDE I and II: autism brain imaging data exchange I and II. HC: healthy control, ASD: autism sepectrum disorder, ADHD: attention-deficit/hyperactivity disorder, SPR: schizophrenia, MRI: magnetic resonance imaging, sMRI: structural MRI, DTI: diffuse tensor imaging, rfMRI: resting state functional MRI, tfMRI: task-based function MRI, fNIRS: functional near-infrared spectroscopy, WB: whole brain, ROI: region of interest, PFC: prefrontal cortex, ICA: independent component analysis, PCA: principal component analysis, SAE: sparse auto-encoder, CNN: convolutional neural network, FCC ANN: fully connected cascade artificial neural network, SVM-RFE: support vector machine-recursive feature elimination, DBN: deep belief network, SVM: support vector machine, LSTM: long short-term memory, CGRNN: consisting of CNN and GRU, WEKA: waikato environment for knowledge analysis

J Korean Acad Child Adolesc Psychiatry 2020;31:97-104 https://doi.org/10.5765/jkacap.200021
© 2020 J Korean Acad Child Adolesc Psychiatry