Table 6 Characteristics of overall specificity of AI in caries detection
From: Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis
Sl No | Authors Year | N | Specificity (%) | Specificity 95% CI | Specificity OR |
|---|---|---|---|---|---|
1 | Chen et al., 2023 [17] | 11,000 | 95.47%, | 0.94, 0.96 | 4.88 (4.26,5.66) |
2 | Bayraktar et al., 2022 [19] | 1000 | 98.19%, | 0.97, 0.99 | 54.25 (32.33, 99.0) |
3 | Huang et al., 2021 [20] | 748 | 89.83% | 0.88, 0.92 | 8.83 (7.33, 11.50) |
4 | Vinayahalingam et al. 2021 [25] | 500 | 88.0 | 0.85, 0.91 | 7.33 (5.67, 10.11) |
5 | Zheng et al., 2021 [29] | 844 | 82.0 | 0.79, 0.84 | 4.56 (3.76, 5.25) |
6 | Devlin et al.,:2021 [31] | 24 | 11% | 0.06, 0.16 | 0.12 0(.06, 0.19) |
7 | Cantu et al., 2020 [34] | 3293 | 83.0 | 0.81, 0.85 | 4.89 (4.26, 5.77) |