Performance Validation of CEPH_2D, a Novel Artificial Intelligence Tool for Automatic Cephalometric and Obstructive Sleep Apnea Syndrome Analyses

Background/Objectives: Cephalometric analysis is essential in orthodontics and for studying conditions such as obstructive sleep apnea syndrome (OSAS). However, manually identifying anatomical landmarks and segmenting the pharyngeal airway on lateral cephalograms can be time-consuming and prone to errors. This study evaluates the CEPH_2D system, an AI-based tool designed to automate cephalometric landmark detection and pharyngeal airway segmentation from 2D lateral cephalometric radiographs. Methods: The system was evaluated on 35 anonymized lateral cephalograms obtained from patients aged 6–65 years, including mixed and permanent dentition cases. Two experienced clinicians generated and reviewed the ground truth annotations for cephalometric landmark localization and pharyngeal airway segmentation. System performance was assessed using mean radial error (MRE), successful detection rate (SDR), mean average precision (mAP), Dice similarity coefficient (DSC), precision, recall, and inference time. Results were compared with manual methods and existing automated tools. Results: The system reached a mean radial error (MRE) of 0.740 ± 0.793 mm for the key point detection task and a mean Dice Score (mDSC) of 0.935 ± 0.040 with an average processing time of 2.557 ± 0.504 s. Conclusions: CEPH_2D appears to be a promising adjunctive tool for automatic cephalometric landmark detection and pharyngeal airway segmentation on lateral cephalograms, although clinician verification remains advisable before clinical interpretation or treatment planning, particularly for landmarks showing higher detection errors.