# Diagnostic Accuracy and Agreement Between AI and Clinicians in Orthodontic 3D Model Analysis
## 1 Introduction
Angle classification describes sagittal dental relationships using the maxillary first molar as a reference point. Class I is diagnosed when the mesiobuccal cusp of the maxillary first molar fits into the mesiobuccal groove of the mandibular first molar. A distal relationship of the mandibular first molar indicates Class II, while a mesial relationship indicates Class III [23]. This classification allows accurate evaluation of the anteroposterior dental relationship and plays a central role in diagnosing malocclusion and establishing individualized treatment plans [24]. These diagnostic parameters and analyses play a crucial role in orthodontic diagnosis and treatment planning by enhancing clinical decision-making and treatment efficiency. Traditionally, orthodontic measurements were performed manually on plaster models. However, challenges such as storage requirements, the risk of damage, and potential data loss have led to the widespread adoption of digital models as a preferred alternative [25; 26]. Although two dimensional digital images were initially introduced for archiving purposes, the use of three-dimensional digital models capable of supporting comprehensive analyses has largely replaced plaster model assessments in clinical orthodontics alternatives [26]. [PERSON] et al. [25] reported that digital measurements of mesiodistal widths, arch length and width, overjet, and overbite are comparable in accuracy and repeatability to those obtained from traditional plaster models. Despite these technological advancements, manual measurements on digital models remain time consuming and are subject to variability among clinicians [26; 27]. To overcome these limitations, artificial-intelligence-assisted automation has been introduced to reduce clinical workload, minimize diagnostic inconsistencies, and improve the overall quality of care [14; 27; 28; 29; 30]. Recent studies have demonstrated that AI-supported systems enhance diagnostic consistency by reducing human error and increasing standardization across clinical assessments [25]. These tools also accelerate data processing, improve decision-making accuracy, and support more efficient treatment planning [27; 29]. In light of these developments, artificial intelligence has become increasingly integrated into orthodontic workflows, offering practical solutions that streamline routine processes while supporting better clinical outcomes. Nevertheless, ongoing validation remains essential to ensure the reliability and appropriateness of these systems in everyday practice. The aim of this diagnostic accuracy study was to compare six key components of digital orthodontic model evaluation, namely overjet, overbite, Angle classification, space analysis, anterior Bolton analysis, and overall Bolton analysis, between assessments performed by experienced orthodontists and those generated by AI-based software. The alternative hypothesis (H\({}_{1}\)) was that AI-based 3D model analysis tools would show statistically significant differences and variable diagnostic agreement compared to clinician assessments across both numerical and categorical orthodontic parameters.