## 4 Discussion  
Two-dimensional radiographic volume measurements are considered insufficient due to the limited visualization of anatomical structures [1]. Consequently, the use of three-dimensional imaging methods, which provide more comprehensive anatomical information, has been increasingly evaluated. CBCT offers significant advantages over conventional methods by providing higher accuracy and precision in airway and condylar volume measurements, particularly in the detailed assessment of bony structures. The rapid acquisition of CBCT scans enhances patient comfort while minimizing motion artifacts. Additionally, compared to conventional computed tomography (CT), CBCT is more cost-effective and exposes patients to lower radiation doses, making it increasingly preferred in clinical applications [27, 28]. However, CBCT has certain limitations. Unlike magnetic resonance imaging (MRI), it does not provide soft tissue contrast, which restricts the detailed analysis of the airway and surrounding soft tissues [17]. Furthermore, CBCT is a static imaging method and lacks the capability to assess airway function in real-time. Therefore, in cases requiring dynamic respiratory analysis, MRI or dynamic CT should be used as complementary imaging modalities [11]. In the present study, airway volume measurements were performed semi-automatically, with the mandible fully segmented using AI-assisted methods from DICOM files. Condyles were separated in accordance with the literature, and their volumes were measured using B4D [13, 26]. These AI-assisted volume measurements were found to be highly consistent. Compared to manual segmentation, AI-assisted segmentation of anatomical structures not only allows segmentation to be completed in a shorter time but also eliminates potential human errors [21]. The accuracy of AI-assisted segmentation was visually assessed using a DICOM viewer and compared with results from another AI platform, DentalMesh AI. A strong level of agreement was observed between the two. The use of AI in evaluating anatomical structures and understanding their relationships has the potential to enhance our knowledge of human craniofacial development. In individuals with skeletal Class I, different vertical growth patterns can develop. In this study, skeletal Class I individuals were categorized into three groups based on their vertical growth patterns: normodivergent, hyperdivergent, and hypodivergent. Existing research explores the association between vertical growth patterns and pharyngeal airway dimensions [6; 29; 30]. [PERSON] et al. [29] examined pharyngeal airway volume across various vertical growth patterns using CBCT and concluded that total airway volumes did not differ significantly. Differences in sample selection criteria, such as anatomical variation and age distribution, may have influenced these results. Additionally, variations in segmentation algorithms and measurement techniques could explain these discrepancies. Unlike these studies, the present study found that the hyperdivergent group had significantly lower airway volumes compared to the normodivergent and hypodivergent groups (\(p<0.05\)). Hyperdivergent individuals often exhibit mandibular backward rotation, which can narrow the pharyngeal airway by displacing the tongue and soft palate, increasing airway resistance. Additionally, increased facial height and weak masticatory muscle activity may further contribute to reduced airway width. These factors may lead to increased airway resistance and a higher risk of obstructive sleep apnea (OSA) in hyperdivergent individuals [31]. This finding indicates that hyperdivergent growth patterns should be carefully considered in orthodontic treatment planning. Since hyperdivergent individuals exhibit smaller condylar volumes and narrower airway dimensions, evaluating airway patency and mandibular growth potential before initiating orthodontic treatment is crucial.