Int J Stomatol

Int J Stomatol ›› 2025, Vol. 52 ›› Issue (2): 272-280.doi: 10.7518/gjkq.2025010

• Reviews • Previous Articles    

Progress in research on digital methods for the morphological rehabilitation of maxillofacial defects

Qingzhao Qin1(),Aonan Wen1,Zixiang Gao2,Yujia Zhu1,Yong Wang1,2,Yijiao Zhao1,2()   

  1. 1.Peking University School and Hospital of Stomatology & Center of Digital Dentistry & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & National Health Commission Key Laboratory of Digital Stomatology (Key Laboratory of Digital Stomatology, Chinese Academy of Medical Sciences) & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
    2.Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
  • Received:2023-12-05 Revised:2024-07-30 Online:2025-03-01 Published:2025-03-01
  • Contact: Yijiao Zhao E-mail:1526678223@qq.com;kqcadcs@bjmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(82271039);National Key R&D Program of China(2022YFC2405401);Key R&D Program of Gansu Province of China(21YF5FA165);Peking University Medicine Fund for World's Leading Discipline or Discipline Cluster Development(BMU2022XKQ003);Open Subject Foundation of Peking University Hospital of Stomatology(PKUSS20220301)

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Abstract:

Severe maxillofacial defects considerably affect the facial appearance and physiological functions of patients. Advancements in digital technologies have focused the attention of research toward the application of various digital solutions for the morphological restoration of maxillofacial defects. This review systematically examines algorithmic research focusing on morphological restoration designs for maxillofacial defects. Based on algorithmic principles, these methods are categorized into four types: classical methods (mirroring and database methods), model deformation method, statistical shape model method, and machine learning algorithms. The review analyzes and evaluates the principles and characteristics of these metho-ds, aiming to provide insights into clinical applications and research in the field of oral healthcare.

Key words: jaw defects, jaw reconstruction, facial defetcs, prosthesis, digital design

CLC Number: 

  • R782.2

TrendMD: 

Tab 1

Horizontal comparison table of four digital methods for maxillofacial defect repair design"

项目镜像法、数据库法模型变形法SSM法机器学习算法
原理

镜像法：健侧数据镜像至患侧，作为缺损修复的参考

数据库法：从数据库中检索合适的完整数据，作为缺损修复的参考

将参考模型变形匹配到缺损模型上，作为缺损修复的参考。参考模型可以为颌面平均形状、健侧镜像数据、数据库检出数据首先基于正常颌面部数据建立参数化模型，然后通过提取缺损数据的形状参数，计算生成个性化目标参照数据基于学习到的颅颌面先验形状特征知识，实现缺损补全
适用缺损类型

镜像法：单侧缺损

数据库法：跨中线缺损

单侧缺损及跨中线缺损单侧缺损及跨中线缺损单侧缺损及跨中线缺损
优势临床最常用，可解决大部分单侧、跨中线缺损修复设计的临床需求提高了修复设计的自动化程度，降低医生经验依赖性，提高了缺损重建效果提高了修复设计的自动化程度，降低医生经验依赖性具有自动化、个性化与智能化的优势
局限性医生经验依赖性强；修复设计耗时费力；镜像法只适用单侧缺损；数据库法的数据库在不同地区、医院之间缺乏通用性修复重建效果受缺损数据与参考模型相似程度的影响生成的目标参照数据与缺损数据的表面不完全匹配重叠目前在颜面缺损修复设计中的研究较少
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