# Diagnostic Accuracy and Agreement Between AI and Clinicians in Orthodontic 3D Model Analysis
## 4 Discussion
However, the presence of statistically significant differences does not always indicate clinical relevance. In such cases, it becomes crucial to interpret the differences in relation to accepted clinical thresholds. Some recent investigations have instead used two-dimensional intraoral photographs for AI-based diagnostic evaluation limits [13; 49]. In one of these studies, [PERSON] et al. [13] assessed the diagnostic accuracy of an AI-based tool (SmileMate) using standardized clinical photographs and reported only slight to moderate agreement with clinician assessments [13]. For comprehensive diagnostic evaluation and precise orthodontic treatment planning, the integration of artificial intelligence with 3D intraoral scan data holds considerable potential. Accordingly, the performance of currently tested AI models may provide valuable reference points for assessing the applicability of AI in clinical orthodontic diagnostics. Previous studies have investigated the validity and efficiency of digital model analyses in orthodontics, ranging from semi-automated software to fully AI-based platforms. [PERSON] et al. [27] compared a semi-automated digital model analysis tool (OnyXCeph3) with conventional manual measurements and reported minimal measurement differences, most of which were clinically insignificant. However, their approach still required operator input and did not involve artificial intelligence. In contrast, a recent study by [PERSON] et al. [30] evaluated the diagnostic performance of fully AI-driven software (LaonSetup) using 3D intraoral scans. While they reported strong agreement with orthodontists in numerical parameters such as overjet and overbite, their analysis did not include categorical diagnostic variables, such as molar classification or the localization of tooth size discrepancies. In contrast, the present study distinguishes itself by employing fully AI-based diagnostic software platforms and by incorporating categorical evaluations, including Angle classification and the location of tooth size discrepancies. Moreover, statistically significant differences were observed between the Orthodontist group and the AI platforms for several parameters assessed in our study. This discrepancy may be attributed to insufficient training of the AI models or inherent algorithmic limitations within the platforms used. [PERSON] anticipated that the integration of larger and more diverse datasets could enhance the diagnostic performance of these systems. Recent studies have explored the integration of AI into digital orthodontic workflows, particularly in tooth segmentation, reporting high accuracy and efficiency across various algorithms and complex anatomical regions [45; 50]. In contrast, the present study offers a complementary perspective by shifting the emphasis from segmentation accuracy to the diagnostic reliability of AI-derived measurements in clinical orthodontic practice. To date, only a limited number of studies [30] have addressed this aspect, which underscores the novelty of our investigation. The present study revealed that, despite the high internal consistency observed within both AI systems, no agreement was found between the Orthodontist group and either platform in identifying the arch (maximly or mandibular) where the anterior or overall Bolton discrepancies were located. The Cohen's kappa values for these comparisons were close to zero or even negative, indicating a lack of categorical concordance. Moreover, inter-examiner agreement among orthodontists was only substantial for the anterior region (\(\kappa\) = 0.71) and moderate for the overall assessment (\(\kappa\) = 0.467), underscoring both the subjectivity of manual Bolton analyses and the difficulty of achieving consistent evaluations even among experienced clinicians (Table 2). Furthermore, notable inconsistencies were observed in parameters that require more complex anatomical landmark detection, such as overjet, overbite, and space analyses.