Table 1.
Authors | DL Models |
Year | Training Dataset |
Validation/Test Dataset | Functions | Best Performance of DL | Time- Consuming |
---|---|---|---|---|---|---|---|
Jacobs et al. [28] |
3D U-Net | 2021 | 48 | 25 | Segmentation of pharyngeal airway space | Precision: 0.97 ± 0.02 Recall: 0.98 ± 0.01 Accuracy: 1.00 ± 0.00 DSC: 0.98 ± 0.01 IoU: 0.96 ± 0.02 95HD: 0.82 ± 0.41 mm |
No |
Choi et al. [29] |
CNN | 2021 | 73 for segmentation 121 for OSAHS diagnose |
15 for segmentation 52 for OSAHS diagnose |
Segmentation of upper airway, computational fluid dynamics and OSAHS assessment |
Sensitivity: 0.893 ± 0.048 Specificity: 0.593 ± 0.053 F1 score: 0.74 ± 0.033 DSC: 0.76 ± 0.041
Sensitivity: 0.893 ± 0.048 Specificity: 0.862 ± 0.047 F1 score: 0.0876 ± 0.033 |
6 min |
Yuan et al. [30] |
CNN | 2021 | 102 | 21 for validation 31 for test |
Segmentation of upper airway | Precision: 0.914 Recall: 0.864 DSC: 0.927 95HD: 8.3 |
No |
Spampinato et al. [31] |
CNN | 2021 | 20 | 20 | Segmentation of sinonasal cavity and pharyngeal airway | DSC: 0.8387 Matching percentage: 0.8535 for tolerance 0.5 mm 0.9344 for tolerance 1.0 mm |
No |
Oz et al. [32] |
CNN | 2021 | 214 | 46 for validation 46 for test |
Segmentation of upper airway | DSC: 0.919 IoU: 0.993 |
No |
Lee et al. [33] |
Regres-sion Neural Network | 2021 | 243 | 72 | Segmentation of upper airway | r2 = 0.975, p < 0.001 | No |