## 3 Results
### Meta-Analysis  
A total of ten articles ([PERSON] et al., 2014 [12]; [PERSON] et al., 2018 [13]; [PERSON] et al., 2020 [14]; [PERSON] et al., 2020 [15]; [PERSON] et al., 2021 [16]; [PERSON] et al., 2021 [17]; [PERSON] et al., 2022 [18]; [PERSON] et al., 2022 [19]; [PERSON] et al., 2023 [20]; and [PERSON] et al., 2024 [21]) were examined in a study to explore the differences in the identification of anatomical landmarks between deep learning and manual approaches. Taking into account the diverse designs of the studies, a random-effects model was used. The high level of \(l^{2}\) values (99%; \(p<0.01\)) indicated substantial heterogeneity among the studies. To account for this heterogeneity, a subgroup analysis was performed, dividing the literature into two subgroups based on the type of radiographic analysis and cone-beam computed tomography. The results of the meta-analysis showed that the pooled mean difference for accuracy in detecting the anatomical landmarks between artificial intelligence and manual approaches was 0.35 (95% CI, \(-0.09\) to 0.78), and the overall effect between the groups was insignificant (\(p>0.01\)) (Figure 3).  
Seven articles ([PERSON] et al., 2024 [21]; [PERSON] et al., 2022 [19]; [PERSON] et al., 2022 [18]; [PERSON] et al., 2021 [16]; [PERSON] et al., 2020 [15]; [PERSON] et al., 2020 [14]; and [PERSON] et al., 2018 [13]) investigated the accuracy of detecting anatomical landmarks using cephalometric radiographs. The studies revealed a high level of heterogeneity (\(l^{2}\) values of 99%; \(p<0.01\)). The meta-analysis revealed a pooled mean difference of 0.09 (95% CI, \(-0.18\) to 0.36; \(p>0.01\)) between artificial intelligence and manual approaches in detecting anatomical landmarks (Figure 3).  
Three articles ([PERSON] et al., 2023 [20]; [PERSON] et al., 2021 [17]; and [PERSON] et al., 2014 [12]) assessed the disparities in identifying anatomical landmarks using cone-beam computed tomography. The high level of \(l^{2}\) values (99%; \(p<0.01\)) indicated substantial heterogeneity among the studies. The meta-analysis showed a pooled mean difference of 0.95 (95% CI, \(-0.21\) to 2.11; \(p>0.01\)) between artificial intelligence and human in detecting anatomical landmarks. The forest plot did not demonstrate superiority between artificial intelligence and humans (Figure 3).  
Figure 2: Results of risk of bias assessment with its graphic representation and bar plot of risk of applicability concerns assessment through QUADAS-2.  
Six articles ([PERSON] et al., 2018 [13]; [PERSON] et al., 2021 [16]; [PERSON] et al., 2021 [17]; [PERSON] et al., 2022 [18]; [PERSON] et al., 2022 [19]; and [PERSON] et al., 2024 [21]) were examined in a study to access the successful detection rate (SDR) of deep learning with a margin of error of 2 mm, considering that a margin of error of 2 mm is clinically acceptable ([PERSON] et al., 2021 [16]). Given the diverse designs of the studies, a random-effects model was used. The high level of \(I^{2}\) values (99%; \(p<0.01\)) indicated very high heterogeneity among the studies. The meta-analysis showed a pooled proportion of 0.77 (95% CI, 0.69 to 0.84). The forest plot demonstrates relatively consistent high SDR of deep learning with a margin of error of 2 mm above 70%, with the exception of one article ([PERSON] et al., 2021 [17]) (Figure 4).