Int J Stomatol

Int J Stomatol ›› 2026, Vol. 53 ›› Issue (5): 615-623.doi: 10.7518/gjkq.2026628

• Expert Forum •    

Research progress in the intelligent cephalometric analysis of three-dimensional craniomaxillofacial soft and hard tissues

Luwei Liu(),Bin Yan()   

  1. Dept. of Orthodontics, Affiliated Stomatological Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing 210029，China
  • Received:2026-03-01 Revised:2026-06-03 Online:2026-09-01 Published:2026-08-28
  • Contact: Bin Yan E-mail:liuluwei_orth@njmu.edu.cn;byan@njmu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2023YFC2413605);International Cooperation Project of Jiangsu Science and Technology Key R&D Plan(BZ2023042);National Online Course Research Project for Graduate Students in Medicine and Pharmacy(2024-28)

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Abstract:

In recent years, deep learning has achieved remarkable advances in medical image analysis. Automated landmark identification and cephalometric analysis of craniofacial hard and soft tissues are key components of three-dimensional (3D) craniofacial assessment. These techniques have shown great potential for improving measurement efficiency, reducing interobserver variability, and supporting personalized diagnosis and treatment planning. This article reviews the development of two-dimensional automated cephalometric analysis and summarizes recent progress in deep learning-based methods for 3D automated landmark identification and cephalometric analysis of craniofacial hard and soft tissues. Major challenges are discussed, including the lack of standardized measurement protocols, dependence on large annotated datasets, limited model generalizability, and insufficient clinical interpretability. Future perspectives are also presented, with a focus on the establishment of large-scale clinical databases, the development of standardized measurement systems, and the application of multimodal and multitask models for clinical decision support. This review aims to provide a reference for future research and clinical translation of 3D automated craniofacial landmark identification and cephalometric analysis.

Key words: three-dimensional cephalometry, craniomaxillofacial structure, automatic landmark detection, deep learning, orthodontics

CLC Number: 

  • R783.5

TrendMD: 

Fig 1

Framework of deep learning models for 3D automated landmark identification"

Tab 1

Relevant studies on automated landmark identification in three-dimensional craniofacial data (2023-2025)"

学者模型构架数据样本量

定位

对象

训练集样本量测试集样本量关键点数量定点准确性

Liu等［18］

改进3D U-Net＋体素热图回归

1 190例（800例SCT+390例CBCT；内部测试720例，外部测试/推理470例）

SCT＋CBCT

504例（SCT 336例+CBCT 168例；另设验证集72例）144例内部测试（SCT 96例+CBCT 48例）+ 470例外部测试/推理（SCT 320例+CBCT 150例）

SCT 41个点；CBCT 14个点

总体 MRE为1.22 mm±0.72 mm

Jiang等［25］3D CNN注意力回归网络＋直接回归3D坐标

498例

CBCT

-

-

43个点（颅骨5，上颌8，下颌9，牙槽及牙列10，软组织11）总体 MRE为1.76 mm±1.13 mm
Tang等［21］Mean-Teacher半监督框架+3D V-Net211例CBCT192例19例18个骨性标志点平均误差为1.91 mm±1.17 mm

Tanikawa等［26］

基于CBCT表面重建的3D网格+AI识别标志点

185例

CBCT152 例33 例64个点（颅骨19 个点+下颌骨45个点）上颌骨、下颌骨标志点平均定位误差分别为0.80 mm±0.57 mm、1.45 mm±0.34 mm
Sahlsten等［27］轻量级基于图像块的3D深度网络+热图回归309例（芬兰 199 例，泰国 110 例；训练/验证/测试：178/28/103例）CBCT178例103例46个点（骨性+牙性）芬兰、泰国队列平均定位误差分别为1.99 mm±1.55 mm、1.96 mm±1.25 mm
Deng等［28］深度学习61 例--61 例68个点（骨性+牙性+部分软组织点）MRE为2.3~2.4 mm
Tao等［29］两阶段3D卷积神经网络/U-Net80例（颌面畸形患者）SCT58例22例77个点（覆盖颅底、上颌、下颌及牙列）总体 MRE为1.81 mm± 0.89 mm
Nishimoto等［30］多阶段Deep learning networks公共数据库120例CT90例30例16个骨性标志点平均定位误差为2.88像素（约2.81 mm）
Blum等［19］三阶段的3D U-Net algorithm1 045例CBCT931例114例35个骨性标志点总体MRE为2.73 mm
Gillot等［31］深度强化学习143例中视野和大视野的CBCTCBCT--32个骨性标志点总体MRE为1.54 mm± 0.87 mm
Berends等［32］DiffusionNet算法2 897例三维颜面扫描2 463例434例10个软组织标志点总体MRE为1.69 mm± 1.15 mm
Al-Baker等［33］卷积神经网络408例三维图像生成10 200例PNG图像（训练/验证/测试：8 160/1 020/1 020例）三维颜面扫描8 160例PNG图像1 020例PNG图像37个软组织标志点总体MRE为0.83 mm±0.49 mm
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