# Diagnostic Accuracy and Agreement Between AI and Clinicians in Orthodontic 3D Model Analysis
## 4 Discussion  
The present study aimed to assess the diagnostic accuracy of two AI-based software platforms, Titan Dental Design and SoftSmile, by comparing their automated measurements with those obtained manually by experienced orthodontists across six key parameters: molar relationship, overbite, overjet, lower arch space analysis, upper arch space analysis, and Bolton analysis. Statistically significant differences were observed in several parameters, including overjet, overbite, and space analysis in both arches, indicating that the AI tools used in this study have not yet reached the desired level of diagnostic accuracy for complex quantitative assessments. However, no statistically significant differences were observed in anterior and overall Bolton analyses when the location of the tooth size discrepancy (maximly or mandibular) was consistent across all groups. In fact, the traditional Bolton ratio itself was not the primary focus of our evaluation. Instead, the study prioritized determining the location of the discrepancy and then quantifying the amount of excess. This decision was based on clinical relevance, as interproximal reduction (IPR) is generally applied based on the specific site and magnitude of the discrepancy rather than the overall Bolton ratio. Notably, the agreement between the orthodontists and the Titan AI system in molar relationship classification was almost perfect, as indicated by high Cohen's kappa values. This suggests that AI systems may be reliably used in categorical occlusal evaluations. In contrast, inconsistencies observed in overjet, overbite, and space analyses are likely attributable to difficulties in accurately identifying the mesial and distal contact points of  
Figure 13: Comparison of Space Analysis Values in the Upper and Lower Jaws Between Orthodontists and the Titan AI. Upper jaw: Wilcoxon signed-rank test (*** \(p<0.001\)). Lower jaw: Paired \(t\)-test (*** \(p<0.001\)). teeth. Although both AI platforms performed adequately in segmenting individual teeth, visual inspection revealed that measurement discrepancies likely stemmed from subtle variations in how the algorithms defined proximal tooth boundaries, rather than from major segmentation errors. These findings highlight the need for further refinement of AI systems, particularly for measurements that require precise anatomical landmark identification. Comprehensive diagnosis and treatment planning are essential components of successful orthodontic practice. Model analysis plays a critical role in this process [16]. It typically includes evaluations such as space analysis, Bolton analysis, dental relationships, and measurements of overjet and overbite. The generation and use of three-dimensional digital models have largely replaced traditional plaster models for these analyses [36]. Digital models can be obtained using various methods, including direct intraoral scanning, scanning of alginate or silicone impressions, digitization of plaster models with laboratory scanners, or imaging techniques such as cone beam computed tomography (CBCT) [37; 38; 39]. This digital transformation has enabled the visualization of both hard and soft tissues in electronic format and has helped establish digital models as a standard in orthodontic record keeping [37; 38]. However, despite the widespread adoption of digital models, manual measurements performed on these models can still increase clinical workload and introduce variability among examiners [26]. To address these challenges, automation solutions powered by AI technologies have emerged, significantly reducing the burden of manual analysis and minimizing diagnostic inconsistencies [14; 30]. These developments have expanded the role of AI beyond model analysis to a broader range of clinical applications in orthodontics [14; 15; 28; 30]. AI is now widely used in diagnosis, treatment planning, and patient monitoring.