Studies
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Sample size
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Method
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Input
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Validation
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Groups
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Parameters
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Results
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Agostinho et al., 2022 [6]
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The internal locally dataset (n=41): AD (n=20), NC (n=21).
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SVM
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MRI, PiB-PET and DTI
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Internal locally dataset and external dataset (ADNI (n=330): AD (n=166), NC (n = 164))
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AD, NC
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AUC, ACC, SEN, SPEC, BACC
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Dependent validation: AD vs NC: MRI: AUC=96%, ACC=92.05%, SEN=86.78%, SPEC=86.78%, BACC=92.05%; PiB PET: AUC=93%, ACC=90.53%, SEN=92%, SPEC=82, BACC=79.84%; DTI: AUC=86%, ACC=76.84%, SEN=76.17%, SPEC=82.09%, BACC=79.84%. Independent validation: AD vs NC: MRI: AUC=81%, ACC=78.02%, SEN=74.12%, SPEC=82.29, BACC=78.20%; PiB PET: AUC=81%, ACC=76.87%, SEN=87.9%, SPEC=68.33%, BACC=78.12%; DTI: AUC=69%, ACC=62.79%, SEN=54.31%, SPEC=71.98%, BACC=63.15%.
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Gao et al., 2022 [7]
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1134 subjects: AD (n=454), NC (n=680).
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3DMgNet (multigrid and convolutional neural network)
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MRI
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10-fold cross-validation and external in-house dataset (AD (n=75), NC (n=59))
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AD, NC
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AUC, ACC, SEN, SPEC
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Dependent validation: ACC=92.13%, AUC=94.43%, SEN=88.42%, SPEC=95%. Independent validation: ACC=87.91%, AUC=95.74%, SEN=79.73%, SPEC=98.31%.
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Goenka et al., 2022 [8]
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769 subjects: AD (n=70), MCI (n=224), NC (475)
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CNN
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MRI
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633 scans from ADNI dataset
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AD, MCI, NC
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AUC, ACC
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Dependent validation: AD vs NC: ACC= 97.83%, AD vs MCI: ACC=98.68%, NC vs MCI: ACC=99.10%, NC vs MCI vs AD: ACC=98.26%. AD vs NC: AUC=94%, AD vs MCI: AUC=97%, NC vs MCI: AUC=99%, NC vs MCI vs AD: AUC=98%.
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Tang et al., 2021 [9]
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560 subjects: AD (n=80), EMCI (n=230), LMCI (n=110), NC (n=140)
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SVM, RF, DT
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MRI
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10-fold cross-validation
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AD, EMCI, LMCI, NC
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AUC, ACC, SEN, SPEC
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RF: NC vs AD: ACC=96.14%, SEN=88.14, SPE=92.81%, AUC=92%. NC vs EMCI: ACC=77.45%, SEN=79.51%, SPE=33.54%, AUC=59%. NC vs LMCI: ACC=87.56%, SEN=64.71%, SPE=83.94%, AUC=81%. EMCI vs AD: ACC=90.15%, SEN=93.51%, SPE=92.43%, AUC=85%. LMCI va AD: ACC=84.54%, SEN=67.91, SPE=72.46%, AUC=89%.
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Dyrba et al., 2021 [10]
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633 subjects: AD (n=189), MCI (n=220), NC (n=254)
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CNN
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MRI and PET
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1-fold cross-validation and three independent datasets: ADNI-3 (n=575), AIBL (n=606), DELCODE (n=474).
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AD, MCI, NC
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AUC, ACC, SEN, SPEC, BACC, PPV, NPV
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Dependent validation: AD vs NC: BACC=88.9%, SEN=94.2%, SPE=83.6%, PPV=81.5%, NPV=95.2% AUC=94.9%. MCI vs NC: BACC=74.5%, SEN=65.5%, SPE=83.6%, PPV=78.1%, NPV=74.1%, AUC=78.5%. amyloid-positive AD vs amyloid-negative NC: BACC=94.9%, SEN=95.6%, SPE=94.3%, PPV=92.7%, NPV=96.6%, AUC=98.5%. amyloid-positive MCI vs amyloid-negative NC: BACC=86.7%, SEN=79%, SPE=94.3%, PPV=91.6%, NPV= 96.6%, AUC=92.5%. Independent validation DELCODE: AD vs NC: BACC=85.5%, SEN=94.2%, SPE=76.7%, PPV=66.2%, NPV=96.5% AUC=95.3%. MCI vs NC: BACC=71%, SEN=65.2%, SPE=76.7%, PPV=66.9%, NPV=75.3%, AUC=77.5%. amyloid-positive AD vs amyloid-negative NC: BACC=83.3%, SEN=95.9%, SPE=70.7%, PPV=73.4%, NPV=95.3%, AUC=96.8%. amyloid-positive MCI vs amyloid-negative NC: BACC=72.2%, SEN=73.7%, SPE=70.7%, PPV=71.2%, NPV= 73.2%, AUC=84%.
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Marzban et al., 2020 [5]
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406 subjects: NC (n=185), MCI (n=106), AD (n = 115)
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CNN
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MRI and DTI
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10-fold cross-validation
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AD,NC, MCI
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AUC, ACC, SEN, SPEC
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AD vs NC: AUC=94%, ACC=93.5%, SEN=92.5%, SPEC=93.9. MCI vs NC: AUC=84%, ACC=79.6%, SEN=62.7%, SPEC=89%
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Li et al., 2020 [11]
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404 subjects: NC (n=268), AD (n=136)
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SVM
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MRI
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10-fold cross-validation and independent validation dataset (AD (n=41), NC (n=25))
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AD, NC
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ACC, SEN, SPEC
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Dependent validation dataset: AD vs NC: ACC=93.07%, SEN=94.12%, SPEC=98.51. Independent validation dataset: AD vs NC: ACC=84.85%, SEN=85.36%, SPEC=84%
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Bae et al., 2020 [12]
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390 subjects: AD (n=195), NC (n=195)
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CNN
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MRI
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5-fold cross-validation and independent validation dataset (AD (n=195), NC (n=195))
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AD, NC
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AUC, ACC, SEN, SPEC
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Dependent validation dataset: AD vs NC: AUC=94%, ACC= 89%, SEN= 88%, SPEC=91%. Independent validation dataset: AD vs NC: AUC=88%, ACC= 83%, SEN= 76%, SPEC= 89%
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Liu et al., 2020 [13]
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449 subjects: AD (n=97), MCI (n=233), NC (n=119)
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CNN
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MRI
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5-fold
cross-validation and independent dataset (AD (n=45),
MCI (n=46), and NC subjects (n=44)).
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AD, MCI, NC
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AUC, ACC, SEN, SPEC
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Dependent validation:
AD vs NC: ACC=88.9%, SEN=86.6%, SPE=90.8%, AUC=92.5%.
MCI vs NC: ACC=76.2%, SEN=79.5%, SPE=69.8%, AUC=77.5%.
Independent validation:
AD vs NC: AUC=89.8%
MCI vs NC: AUC=72.2%
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Zhang et al., 2019 [24]
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857 subjects: NC (n=322), MCI (n=322), AD (n = 213)
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Graph Analysis
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MRI
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Data are randomly partitioned into 80% and 20% for training and testing.
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AD, MCI, NC
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AUC
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AD vs MCI + NC: AUC=73%, NC vs AD + MCI: AUC=72%, MCI vs AD + NC: AUC= 69%.
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Westman et al., 2012 [24]
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369 subjects: AD (n=96), MCI (n=162) and NC (n=111).
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Orthogonal Partial Least-Squares (OPLS)
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MRI, PET, CSF
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7-fold cross-validation
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AD, MCI, NC
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AUC, ACC, SEN, SPEC, PPV, NPV
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AD vs NC: MRI with CSF: ACC= 91.8%, SEN=88.5%, SPEC=94.6%, PPV=93.4%, NPV=90.5% and AUC=95.8%. MRI only: ACC=87%, SEN=83.3%, SPEC=90.1%, PPV=87.9%, NPV=86.2% and AUC=93% CSF only: ACC=81.6%, SEN=84.4%, SPEC=79.3%, PPV=77.9%, NPV=85.4% and AUC=86.1%. MCI vs NC: MRI with CSF: ACC=77.6%, SEN=72.8%, SPEC=84.7%, PPV=87.4%, NPV=68.1% and AUC=87.6%. MRI only: ACC=71.8%, SEN=66.7%, SPEC=79.3%, PPV=82.4%, NPV=62.0% and AUC=81.5%. CSF only: ACC=70.3%, SEN=66.7%, SPEC=75.7%, PPV=80.0%, NPV=60.9% and AUC=74.9%.
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Eskildsen et al., 2012 [47]
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808 subjects: AD (n=194), NC (n=226), pMCI (n=161), sMCI (n=227)
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LDA
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MRI (cortical thickness and age)
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leave-one-out (LOO) validation
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AD, NC, pMCI, sMCI
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AUC, ACC, SEN, SPEC
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Independent feature sets: AD vs NC: ACC=85.5%, SEN=80.4%, SPEC=89.8%, AUC=92%. pMCI vs sMCI: ACC=67.8%, SEN=64.6%, SPEC=70%, AUC=68.2%. Dependent feature sets: AD vs NC: ACC=87.4%, SEN=82.5%, SPEC=91.6%, AUC=93.1%. pMCI vs sMCI: ACC=68.3%, SEN=67.7%, SPEC=68.7%, AUC=74.7%.
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