Performance Validation of PAN_CURVE, a Novel Artificial Intelligence Tool for the Automatic Detection of Panoramic Curves from CBCT X-Rays

Abstract

Background/Objectives: Detecting panoramic curves is a critical step in dental imaging, as it serves as the foundation for generating high-quality panoramic radiographs from Cone Beam Computed Tomography (CBCT) scans. These curves trace the dental arch, ensuring that key anatomical structures, such as teeth, alveolar ridges, and jaws, are accurately represented in a single 2D image. This study aimed to evaluate the performance of the PAN_CURVE system across diverse clinical scenarios. Methods: The system was evaluated in a preliminary external validation performed within a single imaging platform on a dataset of 50 CBCT scans acquired with the same CBCT device and including diverse dental conditions, such as edentulous zones and metal elements like implants and orthodontic devices. Performance was assessed in terms of root mean square error (RMSE), mean absolute error (MAE), and processing time. Results: The system achieved an average RMSE of 1.55 ± 2.91 mm and a MAE of 1.28 ± 2.43 mm. Its processing time averaged 4.70 ± 4.33 s per scan, demonstrating efficiency while meeting usability requirements. Conclusions: These preliminary findings support the potential suitability of the PAN_CURVE system for integration into clinical visualization and planning software for dental and maxillofacial applications; confirmation on larger, multi-device datasets is required before generalized conclusions can be drawn.