Accuracy and Reliability of 3D Cephalometric Landmark Detection with Deep Learning

Objective: Three-dimensional (3D) landmark detection is essential for assessing craniofacial growth and planning surgeries such as orthodontic, orthognathic, traumatic, and plastic procedures. This study aimed to develop an automatic 3D landmarking model for oral and maxillofacial regions and to validate its accuracy, robustness, and generalizability in both spiral computed tomography (SCT, 41 landmarks) and cone-beam computed tomography (CBCT, 14 landmarks) scans. Methods: The model was constructed using an optimized lightweight 3D U-Net network architecture. Its accuracy, robustness, and generalizability were thoroughly evaluated and validated through a multicenter retrospective diagnostic study. The internal dataset included 480 SCT and 240 CBCT cases. For external validation, 320 SCT and 150 CBCT cases were assessed using mean radial error (MRE) and success detection rate within 2-, 3-, and 4-mm error thresholds as the primary evaluation metrics. Error analyses for landmark detection along each coordinate axis were performed. Consistency tests among index observers were conducted. Results: The average MRE for both SCT and CBCT was consistently below 1.3 mm and, notably, below 1.4 mm in complex conditions such as malocclusion, missing dental landmarks, and the presence of metal artifacts. No significant differences in MRE and SDR at 2-4 mm were observed between external and internal SCT and CBCT sets. SCT bone landmarks were more precise than dental ones, with no difference between bone/soft tissue and dental/soft tissue. CBCT dental landmarks exhibited greater precision compared to bone landmarks. A detailed error analysis across the coordinate axes showed that the coronal axis had the highest error rates. The implementation of this model significantly improved the landmarking proficiency of senior and junior specialists by 15.9% and 28.9%, respectively, while also accelerating the process by a factor of 6 to 9.5 times. Conclusions: This study shows that the AI-driven model delivers high-precision 3D localization of oral and maxillofacial structures, even in complex scenarios. The model can aid specialists across all experience levels in conducting accurate and efficient localization analyses, owing to its strong clinical utility, robustness, and generalizability. Clinical Relevance: 3D cephalometric landmark detection is crucial for assessing craniofacial growth and planning diverse surgical procedures, such as orthodontic, orthognathic, trauma, and aesthetic interventions. The traditional manual landmark identification is time-consuming and requires significant expertise. This proposed AI method provides accurate measurements for both soft and hard tissues, streamlines digital planning, decreases reliance on expert knowledge, and enhances the efficiency and success of treatments.