The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

正畸医生和颌面外科医生越来越多地使用 3 维 (3D) 成像来评估复杂的牙颌面畸形和计划正颌手术，这意味着对 3D 头部测量分析的迫切需求。尽管提出了有希望的方法来自动定位 3D 地标，但对鲁棒性和普遍性的担忧限制了它们的临床应用。因此，仍然需要训练有素的操作员来执行手动标记。在这项回顾性诊断研究中，我们旨在训练和评估基于 SpatialConfiguration-Net 的深度学习 (DL) 管道，用于在计算机断层扫描 (CT) 扫描中自动定位 3D 头部测量标志。连续术前 CT 扫描的回顾性样本随机分布在训练/验证集（n= 160）和一个测试集（n = 38）。参考数据由 33 个地标组成，由 1 个操作员手动定位一次（n = 178）或由 3 个操作员手动定位两次（n= 20，仅测试集）。在对测试集进行推断后，1 次 CT 扫描显示“非常低”的置信水平预测；我们将其排除在整体分析之外，但仍然评估和讨论了相应的结果。通过将预测与参考数据进行比较来评估模型性能；结果集包括定位精度、头影测量以及与手动标记可重复性的比较。在保持测试集上，平均定位误差为 1.0 ± 1.3 mm，而 2.0、2.5 和 3.0 mm 的成功检测率分别为 90.4%、93.6% 和 95.4%。角度和线性测量的平均误差分别为 -0.3 ± 1.3° 和 -0.1 ± 0.7 mm。与手动重现性相比，测量结果在 Bland-Altman 95% 的一致范围内，分别为 91.9% 和 71。分别为 8% 的骨骼和牙槽变量。总而言之，虽然我们的 DL 方法仍需要改进，但它在具有挑战性的测试集上提供了高度准确的 3D 地标定位，并且骨骼评估的可靠性与临床医生获得的结果相当。