Table 3 Characteristics of the included studies are as follows
From: Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis
Sl | Authors Year | Study Design | AI algorithm | No. of sample | X-ray type | Comparator | Evaluation | Outcomes |
|---|---|---|---|---|---|---|---|---|
1 | Chen et al 2023 [17] | Retrospective | CNNs, Efficient Net | 11,000 | Digital periapical radiographs | Trained dental professionals | Accuracy of 95.44%, sensitivity 94.15%, specificity 95.47%, ROC AUC 98.31% | With convolutional neural networks (CNNs), the EfficientNet-B0 model outperforms the status quo |
2 | Zhu et al 2022 [18] | Retrospective | CNNs | 1159 | Panoramic radiographs | Reference models | Accuracy: 93.64%, precision: 94.09%, mean dice coefficient: 93.64%, F1 score: 92.87%, and recall: 86.01% | The CNN model successfully separated the caries from the panoramic x-rays |
3 | Bayraktar et al 2022 [19] | Cross sectional | CNNs | 1000 | Digital bitewing radiographs | 2 Trained dental professionals | Accuracy of 94.59%, sensitivity was 72.26, specificity was 98.19%, PPV was 86.58%, NPV was 95.64% and overall AUC was 87.19% | With respectable accuracy rates, this CNN-based model performed admirably |
4 | Huang et al 2021 [20] | Cross sectional | CNNs | 748 | OCT and micro-CT images | Dental surgeons | Accuracy 95.21%, Sensitivity of 98.85% specificity of 89.83%, PPV of 93.48% and NPV was 98.15%, | ResNet-152 When comparing OCT pictures of teeth, CNNs models outperform physicians in identifying abnormal structures |
5 | Mao et al 2021 [21] | Cross sectional | CNNs | 278 | Bitewing radiographs | Reference models GoogleNet, Vgg19, and ResNet50 | Accuracy 95.56% | AlexNet model demonstrated high accuracy in comparison to other models |
6 | De Araujo Faria et al. 2021 [22] | Retrospective | ANNs | 15 | Digital Panoramic radiographs | 2 Expert dental surgeon | Accuracy of 98.8% AUC = 0.9869 | The model's ability to detect and diagnose caries was highly accurate |
7 | Hur et al 2021 [23] | Retrospective | ANNs | 2642 | Panoramic radiographs and CBCT images | Single predictors as reference | ROC of 0.88 to 0.89 | ANNs have better accuracy |
8 | Lee et al 2021 [24] | Cross sectional | CNNs | 354 | Bitewing radiographs | dental surgeon | Precision 63.29%; recall 65.02%; F1-score 64.14% | This CNNs model displayed significant performance in detecting caries lesion |
9 | Vinayahalingam et al. 2021 [25] | Retrospective | CNNs | 500 | Panoramic radiographs | Reference standards | Accuracy of 0.87, sensitivity of 0.86, specificity of 0.88, AUC of 0.90, F1 score of 0.86 | This AI model displayed skilled performance in detecting caries in third molars |
10 | Mertens S et al 2021 [26] | Randomized control trial | CNNs | 140 | Bitewing radiographs | dental surgeon | ROC of 0.89 sensitivity of 0.81 | AI model demonstrated statistically significant performance compare to dentist |
11 | Moran et al 2021 [27] | Cross sectional | CNNs | 112 | Digital bitewing radiographs | ResNet model | Accuracy of 73.3% | Better accuracy compare to the reference model |
12 | Lian et al 2021 [28] | Cross sectional | CNNs | 1160 | Panoramic radiographs | dental surgeon | IoU 0.785, Dice coefficient values of 0.663 Accuracy of 0.986 recall rate of 0.821 | These models displayed similar results to that of expert dentists |
13 | Zheng et al 2021 [29] | Cross sectional | CNNs | 844 | Radiographs | VGG19, Inception V3, dental surgeon | Accuracy = 0.82, precision = 0.81, sensitivity = 0.85 specificity = 0.82, AUC = 0.89, | CNN model ResNet18 showed good performance |
14 | Bayrakdar et al 2021 [30] | Retrospective | CNNs | 621 patients (2325 images, 2072 for training, 200 for validating and 53 for testing) | Bitewing radiographs | dental surgeon | For caries detection sensitivity 0.84, precision 0.81, and F-measure rates 0.84 and for caries segmentation were sensitivity 0.86, precision 0.84, and F-measure rates 0.84 | These models can accurately detect DC. There were also beneficial in the segmentation of DC |
15 | Devlin et al 2021 [31] | Randomized control trial | CNNs | 24 | Bitewing radiographs | dental surgeon | High accuracy of diagnosis with sensitivity of 71% and decrease in specificity of 11% are statistically significant (p < 0.01) in comparison with expert dentists | This model significantly improved dentists’ ability to detect enamel-only proximal caries |
16 | Chen et al 2021 [32] | Retrospective | CNNs | 2900 | Digital periapical radiographs | Reference models with dental surgeon | DC and PDL were detected with precision, recall, and average precision values less than 0.25 for mild level, 0.2–0.3 for moderate level and 0.5–0.6 for severe level Lesions were generally detected with precision and recall between 0.5–0.6 at all levels | This models can detect caries using periapical radiographs |
17 | Geetha et al 2020 [33] | Cross sectional | ANNs | 145 | Digital periapical radiographs | dental surgeon | Accuracy of 97.1%, false positive (FP) rate of 2.8%, ROC area of 0.987 and PRC area of 0.987 | This AI model can predict caries more accurately |
18 | Cantu et al 2020 [34] | Retrospective | CNNs | 3293 | Bitewing radiographs | 4 dental surgeon | Accuracy of 0.80; sensitivity of 0.75, specificity of 0.83 | This CNN-based model was significantly more accurate than the experienced dentists |
19 | Choi et al 2018 [35] | Retrospective | CNNs | 475 | Digital periapical radiographs | Dental surgeon and raw CNN reference models | F1max 0.74 with False Positives 0.88 | This model was superior to the system using a naïve CNN |
20 | Lee et al 2018 [36] | Retrospective | DCNNs | 2400 | Digital periapical radiographs | Not mentioned | Accuracy of 89.0%, 88.0%, 82.0% and AUC of 0.917, 0.890, 0.845 | This CNNs-based model demonstrated good performance in detecting DC |
21 | Srivastava et al 2017 [37] | Retrospective | FCNN | 3000 | Digital periapical radiographs | 3 experienced dentists | F1-score 70%, accuracy 80.5–61.5% | Better performance than comparator dentist |