Research ArticleSpecial Issue

Artificial Intelligence Comparison of the Radiologist Report With Endoscopic Predictors of Successful Transforaminal Decompression for Painful Conditions of the Lumber Spine: Application of Deep Learning Algorithm Interpretation of Routine Lumbar Magnetic Resonance Imaging Scan

Kai-Uwe Lewandrowski, Narendran Muraleedharan, Steven Allen Eddy, Vikram Sobti, Brian D. Reece, Jorge Felipe Ramírez León and Sandeep Shah
International Journal of Spine Surgery December 2020, 14 (s3) S75-S85; DOI: https://doi.org/10.14444/7130

Article Figures & Data

Figures

  • Figure 1
    Figure 1

    Preoperative sagittal (A), axial (B) magnetic resonance imaging scan, posterior-anterior (C), and lateral (D) radiograph of a 48-year-old male. The patient was treated with transforaminal outside-in endoscopic decompression with foraminoplasty and discectomy (E) for failed conservative care of an L4–L5 herniated disc.

  • Figure 2
    Figure 2

    The foraminal severity grading provided by the radiologist plotted for the different foraminal zones showing an average score of 4.71 ± of 2.62 and variable grading across all zones.

  • Figure 3
    Figure 3

    The AI Multus RadBot severity grading plotted for the different foraminal zones showing an average score of 5.65 ± of 3.73 and consistent grading across all zones except in the exit zone.

  • Figure 4
    Figure 4

    The scatter plot of the severity grading (continuous scale) provided by the artificial intelligence Multus RadBot versus the radiologist grading (ordinal scale) showing a nonlinear relationship between these 2 independent predictor variables, with the Multus RadBot consistently grading higher in nearly in all foraminal zones.

  • Figure 5
    Figure 5

    Scatter plot of linear logit model describing the probability of improved clinical outcomes as defined by the dichotomized Macnab criteria predicted by the radiologist ordinal grading.

  • Figure 6
    Figure 6

    Scatter plot of nonlinear logit model describing the probability of improved clinical outcomes as defined by the dichotomized Macnab criteria predicted by the contiguous artificial intelligence grading by the Multus RadBot.

Tables

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  • Table 12
  • Table 13
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Artificial Intelligence Comparison of the Radiologist Report With Endoscopic Predictors of Successful Transforaminal Decompression for Painful Conditions of the Lumber Spine: Application of Deep Learning Algorithm Interpretation of Routine Lumbar Magnetic Resonance Imaging Scan
Kai-Uwe Lewandrowski, Narendran Muraleedharan, Steven Allen Eddy, Vikram Sobti, Brian D. Reece, Jorge Felipe Ramírez León, Sandeep Shah
International Journal of Spine Surgery Dec 2020, 14 (s3) S75-S85; DOI: 10.14444/7130
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