Table 2 Performance metrics with Test B for MP-CNN model vs SP-CNN model.

From: Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis

Algorithm

Model

AUROC (95% CI)

Accuracy (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

PPV (95% CI)

NPV (95% CI)

ResNet50

MP-CNN

0.913 (0.907–0.919)

0.830 (0.805–0.855)

0.784 (0.729–0.839)

0.877 (0.854–0.900)

0.866 (0.845–0.887)

0.803 (0.764–0.842)

SP-CNN(Neu)

0.890* (0.882–0.898)

0.805 (0.789–0.821)

0.800 (0.778–0.822)

0.810 (0.780–0.840)

0.810 (0.787–0.834)

0.801 (0.783–0.818)

SP-CNN(Flx)

0.896* (0.887–0.905)

0.817 (0.810–0.824)

0.780 (0.768–0.792)

0.855 (0.840–0.869)

0.844 (0.832–0.857)

0.794 (0.785–0.802)

SP-CNN(Ext)

0.890* (0.880–0.901)

0.821 (0.801–0.841)

0.800 (0.814–0.871)

0.842 (0.814–0.871)

0.838 (0.817–0.859)

0.809 (0.773–0.845)

VGG19

MP-CNN

0.914 (0.903–0.925)

0.829 (0.810–0.848)

0.782 (0.758–0.806)

0.877 (0.852–0.901)

0.865 (0.842–0.889)

0.802 (0.790–0.813)

SP-CNN(Neu)

0.893* (0.877–0.909)

0.818 (0.789–0.847)

0.778 (0.717–0.839)

0.859 (0.779–0.939)

0.849 (0.783–0.915)

0.804 (0.789–0.818)

SP-CNN(Flx)

0.895* (0.882–0.908)

0.819 (0.798–0.840)

0.782 (0.746–0.818)

0.857 (0.841–0.872)

0.846 (0.829–0.863)

0.790 (0.767–0.813)

SP-CNN(Ext)

0.890* (0.875–0.905)

0.815 (0.804–0.826)

0.774 (0.757–0.791)

0.857 (0.826–0.887)

0.846 (0.821–0.872)

0.813 (0.798–0.838)

VGG16

MP-CNN

0.910 (0.905–0.915)

0.835 (0.829–0.842)

0.782 (0.764–0.800)

0.889 (0.875–0.903)

0.877 (0.865–0.889)

0.803 (0.764–0.842)

SP-CNN(Neu)

0.889* (0.882–0.895)

0.819 (0.806–0.832)

0.796 (0.779–0.813)

0.842 (0.829–0.856)

0.836 (0.823–0.849)

0.801 (0.783–0.818)

SP-CNN(Flx)

0.889* (0.884–0.895)

0.819 (0.804–0.834)

0.770 (0.737–0.803)

0.869 (0.849–0.888)

0.856 (0.839– 0.873)

0.794 (0.785–0.802)

SP-CNN(Ext)

0.892* (0.885–0.898)

0.824 (0.808–0.841)

0.808 (0.777–0.839)

0.840 (0.833–0.848)

0.836 (0.827–0.845)

0.809 (0.773–0.845)

EfficientNet-B1

MP-CNN

0.899 (0.894–0.905)

0.800 (0.792–0.808)

0.714 (0.693–0.735)

0.887 (0.879–0.894)

0.865 (0.859– 0.870)

0.755 (0.742–0.767)

SP-CNN(Neu)

0.883* (0.873–0.893)

0.800 (0.780–0.820)

0.734 (0.701–0.767)

0.867 (0.850–0.884)

0.848 (0.830–0.866)

0.764 (0.741–0.787)

SP-CNN(Flx)

0.877 (0.857–0.898)

0.798 (0.778–0.818)

0.726 (0.697–0.755)

0.871 (0.856–0.885)

0.850 (0.832–0.868)

0.759 (0.738–0.780)

SP-CNN(Ext)

0.866* (0.848–0.884)

0.790 (0.778–0.802)

0.735 (0.714–0.755)

0.846 (0.826–0.867)

0.829 (0.812–0.847)

0.759 (0.746–0.773)

  1. The best values are in bold.
  2. Statistical analysis was performed using a paired t-test to compare the AUROC of each SP-CNN model with that of the MP-CNN model. An asterisk (*) means statistically significant difference (p < 0.05).
  3. AUROC area under the receiver operating characteristics curve, PPV positive predictive value, NPV negative predictive value, CI confidence interval, MP-CNN multi pose-based convolutional neural network model, SP-CNN single pose-based convolutional neural network, Neu neutralposture, Flx flexion posture, Ext extension posture.
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