Accuracy And Generalizability of an Open-Source Deep Learning Model For Facial Bone Segmentation on CT and CBCT Scans

AimTo evaluate the accuracy and generalizability of DentalSegmentator, an open-source deep learning tool, for automated segmentation of skeletal facial surfaces from computed tomography (CT) scans acquired under different imaging conditions. Materials and MethodsTen human skulls were scanned using a CT scanner and three cone beam CT (CBCT) protocols (including an ultra-low-dose protocol) on two CBCT devices. High-accuracy reference surface models were acquired using an optical scanner. CBCT an CT scans were segmented automatically using DentalSegmentator. Three facial regions (forehead, zygomatic process, maxillary process) were defined on each model for quantitative assessment. Accuracy was measured as the mean absolute distance (MAD) and the standard deviation of absolute distances (SDAD) between segmented and reference models after best-fit superimposition. ResultsRepeated segmentations were identical, confirming perfect reproducibility. Across all acquisition settings and regions, DentalSegmentator produced highly accurate skeletal surface models, with an overall MAD of 0.088 mm (IQR 0.073) and SDAD of 0.061 mm (IQR 0.028). Significant but small differences were detected between imaging systems (MAD: p < 0.001; SDAD: p = 0.003), with CT scans showing slightly reduced trueness compared with CBCT images. ConclusionThe open-source DentalSegmentator tool produced accurate skeletal facial surface segmentations across diverse CT and CBCT settings, demonstrating excellent generalizability, including under low-radiation conditions. Minor differences in trueness between imaging systems were small and unlikely to impact clinical or research use. Clinical SignificanceDeep learning offers a robust foundation for automated 3D craniofacial surface extraction, supporting broader adoption of AI-driven workflows in both clinical and research contexts.