Table 3.
Baseline characteristics of the studies included in the review by studying neural networks in orthodontics.
| Study [Ref.] | Year of Publication | Type of Data | Type of Neural Network | Number of Database | Accuracy of Neural Network |
|---|---|---|---|---|---|
| Auconi [33] | 2015 | Cephalometric records | Fuzzy clustering repartition | 54 cephalograms | 83.3% |
| Peilini [34] | 2019 | Medical records | ANN | 302 patients | 94.0% (extraction pattens); 92.8 % (anchorage patterns) |
| Bianchi [36] | 2020 | CBCT blood serum saliva clinical investigation |
Logistic Regression, Random Forest, LightGBM, XGBoost | 52 patients | 82.3% |
| Muraev [39] | 2020 | Cephalometric records | ANN | 330 cephalograms | 99.9% |
| Kök [40] | 2021 | Cephalometric and hand-wrist radiographs | ANN | 419 patients | 94.27% |