Automated 3D cephalometry: A lightweight V-net for landmark localization on CBCT

Cephalometric analysis is a widely adopted procedure for clinical decision support in orthodontics. It involves manual identification of predefined anatomical landmarks on three-dimensional cone beam CT scans, followed by the computation of linear and angular measurements. To reduce processing time and operator dependency, this study aimed to develop a light-weight deep learning (DL) model capable of automatically localizing 16 anatomically defined landmarks. To ensure model robustness and generalizability, the model was trained on a dataset of 350 manually annotated CBCT scans acquired from various imaging systems, covering a wide range of patient ages and skeletal classifications. The trained model is a V-net, optimized for practical use in clinical workflows. The model achieved a mean localization error of 1.95 ± 1.06 mm, which falls within the clinically acceptable threshold of 2 mm. Moreover, the predicted landmarks were used to calculate cephalometric measurements and compare with manually derived values. The resulting errors was -0.15 ± 0.95° for angular measurements and 0.20 ± 0.28 mm for linear ones, with Bland–Altman analysis demonstrating strong agreement and acceptable variability. These results suggest that automated measurements can reliably replace manual ones. Given the clinical relevance of cephalometric parameters - particularly the ANB angle, which is critical for skeletal classification and orthodontic treatment planning - this model represents a promising clinical decision support tool. Additionally, its low computational complexity enables fast prediction, with mean inference time lower than 32 seconds per scan, promoting its integration into routine clinical settings due to both technical feasibility and robustness across heterogeneous datasets. • Full skull CBCT landmarking achieved with light-weight V-net model • Landmark localization error is 1.95 ± 1.06 mm, showing high accuracy • Model performance is clinically acceptable, below 2 mm error threshold • Average inference time per scan is below 32 seconds, supporting clinical use • Bland–Altman shows strong agreement between automated and manual measurements