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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleAdult Brain

An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI

K.A. Wong, A. Hatef, J.L. Ryu, X.V. Nguyen, M.S. Makary and L.M. Prevedello
American Journal of Neuroradiology January 2023, 44 (1) 11-16; DOI: https://doi.org/10.3174/ajnr.A7736
K.A. Wong
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
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A. Hatef
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
bTri-County Radiologists (A.H.), Newark, Ohio
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J.L. Ryu
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
cProScan Imaging (J.L.R.), Columbus, Ohio
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X.V. Nguyen
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
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M.S. Makary
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
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L.M. Prevedello
aFrom the Department of Radiology (K.A.W., A.H., J.L.R., X.V.N., M.S.M., L.M.P.), The Ohio State University College of Medicine, Columbus, Ohio
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  • FIG 1.
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    FIG 1.

    Flow chart describing preparation of training/validation and test sets. The asterisk indicates that due to its infrequency within the data set (1%) and its similarities with the MR imaging brain without/with contrast protocol, the MR imaging for brain metastasis protocol was combined with the MR imaging brain without/with contrast protocol, reducing the total number of protocols from 9 to 8.

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    FIG 2.

    Confusion matrix for the LSTM classifier model. Criterion standard labels are on the y-axis. Labels predicted by the model are on the x-axis. Numbers represent the number of examinations in the test set. WO/W indicates MR imaging brain without/with contrast; MS, MR imaging brain for MS; TUMOR, MR imaging tumor; MENINGIOMA, MR imaging brain meningioma follow-up; SEIZURE, MR imaging brain dedicated seizure with contrast; STEALTH, MR imaging stealth/Stryker/mask/presurgical planning; GAMMA, MR imaging for gamma knife, brain lab, and SRS; CN, MR imaging cranial nerves III–VI without/with contrast.

Tables

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    Table 1:

    Label frequencies of the training/validation and test setsa

    Training/Validation SetOriginal Test Set before Protocol ReviewFinal Test Set after Adjudication
    MR imaging brain without/with contrast11,543 (68.4%)1274 (67.9%)1156 (6.2%)
    MR imaging brain for MS2235 (13.2%)242 (12.9%)251 (13.4%)
    MR imaging tumor963 (5.7%)113 (6.0%)149 (7.9%)
    MR imaging brain meningioma follow-up653 (3.9%)81 (4.3%)117 (6.2%)
    MR imaging brain dedicated seizure with contrast519 (3.1%)65 (3.5%)65 (3.5%)
    MR imaging stealth/Stryker/mask/presurgical planning436 (2.6%)43 (2.3%)45 (2.4%)
    MR imaging for gamma knife, brain lab, and SRS332 (2.0%)41 (2.2%)58 (3.1%)
    MR imaging cranial nerves III–VI without/with contrast201 (1.2%)17 (0.9%)35 (1.9%)
    Total16,88218761876
    • ↵a Data are No. (%).

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    Table 2:

    Performance of the classifier modela

    LabelsWeighted PrecisionWeighted Recall (Accuracy)Weighted F1 Score
    Criterion standard labels0.9080.9050.905
    Original protocol assignments0.8720.8410.850
    • ↵a Precision, recall, and F1 score were calculated for each class, and weighted averages of these metrics were computed using class frequencies as weights. The weighted F1 score represents the average of F1 scores weighted by class frequency.

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    Table 3:

    Contingency table comparing performances of the classifier model and the original protocol assignments on the criterion standard labelsa

    Incorrectly Predicted by Original AssignmentsCorrectly Predicted by Original Assignments
    Incorrectly predicted by model8098
    Correctly predicted by model1851513
    • ↵a Numbers represent number of examinations in the test set that were incorrectly/correctly classified by the model and incorrectly/correctly classified by the original protocol assignments. The McNemar test of homogeneity statistic is 26.13; P value < .001.

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American Journal of Neuroradiology: 44 (1)
American Journal of Neuroradiology
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Cite this article
K.A. Wong, A. Hatef, J.L. Ryu, X.V. Nguyen, M.S. Makary, L.M. Prevedello
An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI
American Journal of Neuroradiology Jan 2023, 44 (1) 11-16; DOI: 10.3174/ajnr.A7736

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AI Tool for Brain MRI Clinical Decision Support
K.A. Wong, A. Hatef, J.L. Ryu, X.V. Nguyen, M.S. Makary, L.M. Prevedello
American Journal of Neuroradiology Jan 2023, 44 (1) 11-16; DOI: 10.3174/ajnr.A7736
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