Table 2.
Main findings of the included studies
| Author, Year, (Country) |
AI Model used | Sample size | Annotator(s)/ Ground truth |
Teeth Identification Task(s) | Performance metrics | Conclusions |
|---|---|---|---|---|---|---|
| Nishitani et al24 2021 (Japan) |
U-Net | 162 images Training set: 82 Validation set: 20 Test set: 60 |
Not mentioned | Semantic segmentation | Jaccard index (0.809) Dice index (0.894) |
U-Net with the new loss function exhibited a higher segmentation accuracy of teeth in panoramic dental X-ray images than that obtained by U-Net with the conventional loss function |
| Leite et al1 2021 (Belgium) |
ResNet 101 | 153 images Training set: 70 Validation set: 18 Test set:65 |
Oral radiologist with 20 years’ experience | Instance segmentation, Time Analysis |
Sensitivity (0.98) Precision (0.99) IoU (0.95) Recall (0.98) F1 score (0.97) Hausdroff distances (7.9) |
The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. Also, the time needed to perform fully manual segmentation of teeth on a panoramic radiograph may be reduced by 67% when using a segmentation method based on deep learning algorithms. |
| Lee et al2
2020 (Korea) |
R-CNN | 30 images 846 teeth |
Oral radiologist with 5 years’ experience | Instance segmentation | Precision (0.85) Recall (0.89) IoU (0.87) F1 score (0.87) Visual analysis |
The method achieved high performance for automation of tooth segmentation on dental panoramic images. The proposed method might be applied in the first step of diagnosis automation and in forensic identification, which involves similar segmentation tasks. |
| Zhao et al21 2020 (China) |
TSASNet | 1500 image Training set :1200 Validation set: 150 Test set:150 |
Not mentioned | Semantic segmentation | Accuracy (0.96) Recall (0.93) Specificity (0.97) Precision (0.94) Dice index (0.92) |
Results showed that TSASNet can obtain superior segmentation performance on dental panoramic images over other state-of-the-art methods and has highly competitive performance compared with the current medical image segmentation methods. |
| Mahdi et al6 2020 (Japan) |
Faster RCNN (ResNet100 +and ResNet 50) |
1000 image Training set: 900 Test set :100 |
Not mentioned | Object detection |
ResNet 50
Precision (0.97) ResNet 101 Precision (0.98) F1 score (0.98) |
The proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry. |
| Muramatsu et al15 2020 (Germany) |
ResNet 50 | 100 images | Dental radiologist | Object detection | Sensitivity (0.96) | High tooth detection sensitivity and classification accuracies were obtained using a limited dataset, suggesting the potential utility of the proposed method in the automatic filing of dental records. |
| Silva et al17
2020 (Bahia) |
Mask R CNN PA Net HTC ResNet |
543 images | Not mentioned | Semantic segmentatin, Instance segmentation, Teeth numbering |
Mask R CNN
Accuracy (0.96) F1 score (0.90) PA Net Accuracy (0.96) F1 score (0.91) HTC Accuracy (0.96) F1 score (0.89) ResNet Accuracy (0.96) F1 score (0.91) |
Results showed that instance segmentation and numbering are feasible to be accomplished by an end-to-end deep network. In our experiments, PANet achieved the best results i.e.F1 score of 91.65% on semantic segmentation |
| Muresan et al25 2020 (Romania) |
ERF Net | 1000 image Training set: 700 Validation set: 100 Test set: 200 |
Not mentioned | Semantic segmentation | Accuracy (0.89) Precision (0.98) Recall (0.91) F1 score (0.93) |
The proposed method solution is able segment accurately the teeth and identify the problems correctly as compared to other methods which are not able to identify all the problems correctly. |