Table 5 Results of the ablation study using different modules in the detection network (D-Net) of SinusC-Net.

From: SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network

Module

Accuracy

Sensitivity

Specificity

SDR (< 2.0 mm)

MRE

convLSTM

MSI

DS

✓

  

0.95 \(\pm\) 0.02

0.87 \(\pm\) 0.01

0.97 \(\pm\) 0.02

93.12 \(\pm\) 0.91

0.92 \(\pm\) 0.11

✓

✓

 

0.95 \(\pm\) 0.03

0.89 \(\pm\) 0.02

0.97 \(\pm\) 0.03

94.34 \(\pm\) 0.89

0.90 \(\pm\) 0.10

✓

✓

✓

0.97 \(\pm\) 0.03

0.92 \(\pm\) 0.02

0.98 \(\pm\) 0.03

95.47 \(\pm\) 0.93

0.87 \(\pm\) 0.14

  1. convLSTM convolutional long short-term memory, MSI multi-scale inputs, DS deep supervision, SDR successful detection rate, MRE mean radial error.
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