## 4 Discussion  
Accurate identification of cephalometric landmarks is crucial in orthodontic diagnosis and treatment planning. Approximately 20% of landmarks identified by AI deviate by more than 2 mm from the ideal location [18]. These landmarks are used for angular and linear cephalometric measurements. A slight deviation in the position of a single landmark can significantly alter the resulting measurement and reduce the diagnostic quality of the traced cephalometric radiograph. Manual adjustment of the misidentified landmarks has been suggested to improve accuracy and allow orthodontists to use the radiograph in a diagnostic manner [11]. Currently, there is no literature that compares the precision and accuracy of AI/human modified landmarks to AI alone. Additionally, no studies have evaluated how less experienced examiners, such as dentists and dental students, fare in cephalometric landmark identification, with or without the aid of AI. As the proportion of orthodontic cases treated by general dentists compared to orthodontists increases, it is important to evaluate the accuracy of these less experienced clinicians. The precision of AI alone obtained in this study was similar to the levels of precision reported in other studies [9; 17]. [PERSON] and his colleagues found that the ICC of AI ranged from 0.768 to 0.997 for the 10 [PERSON]'s measurements. Excluding the lowest ICC measurement of Frankfort plane to true horizontal line (ICC = 0.768), the remaining ICC values were greater than 0.94, indicating excellent precision [17]. This is similar to our study, in that the ICC values of AI traced cephalometric measurements ranging from 0.890 to 1.00 across the 26 cephalometric measurements and demonstrated the greatest precision of all examiners. Furthermore, [PERSON] found that the YOLOv3 deep-learning software had excellent precision and marked each landmark at the exact same position compared to the human inter-examiner reliability of 0.97 mm [9]. The accuracy of AI-derived cephalometric measurements differed from [PERSON]'s study, which found that there were no statistically significant differences in accuracy of nine of the 10 [PERSON]'s measurements used, and AI was unable to measure the 10 th measurement [9]. In this study, 10 out of 26 AI-derived measurements revealed accuracies out of the clinically acceptable range. An interesting note was that of the four of these measurements involved the gonion landmark (posterior face height, FMA, SN-MP, and L1-MP). Posterior face height and L1-MP were significantly underestimated, whereas SN-MP and FMA were overestimated. These errors were consistent with AI software identifying gonion too superiorly relative to the gold standard, which is a likely source contributing to the mean error. This error was not limited to our study, as [PERSON] found that the mean error of YOLOv3 was 2.9 mm in the identification of the gonion landmark [9]. Additionally, a meta-analysis conducted by [PERSON] showed that porion, subspinale, gonion, articulare, and anterior nasal spine were the least accurate landmarks identified by AI, although it varied depending on the specific AI software being tested [18]. Of the five cephalometric measurements that demonstrated moderate precision without the aid of AI, four of them (U1-NA distance, SNA, ANB, and convexity angles) involved \"Point A\" landmark. This suggests \"Point A\" may pose significant challenges in terms of reproducibility. Possibilities for this include the proximity of this point to the maxillary incisor root, superimposition of nearby structures, blurriness of the curvature in the anterior maxilla, and proximity of the canine fossa. Subspinale, also known as Point A, has shown good reliability in the x-axis but significant variation in the y-axis [19]. [PERSON] also reported a