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北京大学学报（医学版） ›› 2021, Vol. 53 ›› Issue (1): 133-138. doi: 10.19723/j.issn.1671-167X.2021.01.020

• 论著 • 上一篇    下一篇

中国人群腭中缝形态特点分期与Demirjian牙龄的相关性

高璐,谷岩()   

  1. 北京大学口腔医学院·口腔医院,正畸科 国家口腔疾病临床医学研究中心 口腔数字化医疗技术和材料国家工程实验室 口腔数字医学北京市重点实验室,北京 100081
  • 收稿日期:2020-08-12 出版日期:2021-02-18 发布日期:2021-02-07
  • 通讯作者: 谷岩 E-mail:guyan96@126.com
  • 基金资助:
    国家自然科学基金(81970979);科技部科技基础资源调查专项课题(2018FY101005)

Chinese morphological stages of midpalatal suture and its correlation with Demirjian dental age

GAO Lu,GU Yan()   

  1. Department of Orthodontics, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
  • Received:2020-08-12 Online:2021-02-18 Published:2021-02-07
  • Contact: Yan GU E-mail:guyan96@126.com
  • Supported by:
    National Natural Science Foundation of China(81970979);Ministry of Science and Technology of China(2018FY101005)

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摘要：

目的: 研究中国人群腭中缝(midpalatal suture,MPS)形态特点分期与Demirjian牙龄间的相关性,以探索牙龄对腭中缝骨化闭合程度的预测能力。方法: 本研究为回顾性研究,收集1 076例样本(女675例,男401例,年龄6.0~20.9岁),观察锥形束CT(cone-beam CT,CBCT)水平位腭中缝区域,记录各样本的MPS分期,同时观察并记录各样本的牙龄。采用Spearman相关系数检验与诊断试验,研究作为有序分类变量的MPS分期与牙龄间的相关关系。结果: (1)左下第二前磨牙:牙龄B~D期者绝大部分处于MPS 1~2期,占95.2%;牙龄E期者MPS 1~2期占85.3%,3期占14.7%;牙龄F期者MPS 3期、1~2期、4s1期分别占45.1%、46.1%和8.8%;牙龄G期者MPS 3期、4s1期、1~2期分别占49.8%、24.9%和18.9%;牙龄H期者大部分处于MPS 4~5期,占80.1%,另16.3%处于3期。(2)左下第二磨牙:牙龄B~D期者绝大部分处于MPS 1~2期,占89.7%;牙龄E期者MPS 1~2期占67.4%,3期占26.1%;牙龄F期者MPS 3期、1~2期、4s1期分别占55.3%、34.2%和10.5%;牙龄G期者MPS 3期、4s1期、1~2期分别占50.7%、24.3%和16.8%;牙龄H期者大部分处于MPS 4~5期,占83.8%;另14.2%处于3期。(3)以牙龄诊断MPS分期,效能较好的配对有:左下第二磨牙牙龄-MPS:H-4s2、H-5、D-1;左下第二前磨牙牙龄-MPS:H-4s2、H-5、G-3。其余配对的诊断效能一般。(4)MPS分期与左下第二磨牙牙龄的Spearman系数最高,为0.68,其次是第二前磨牙,为0.64。(5)若左下第二磨牙或第二前磨牙处于牙龄H期,则该个体很可能超过MPS 4s2期。结论: 除少数诊断效能较好的配对外,牙龄诊断MPS分期的效能总体一般。因此,以牙龄评估腭中缝骨化闭合程度时,应酌情加拍治疗前CBCT,以明确腭中缝骨化闭合状态。

关键词: 腭中缝, 锥束计算机体层摄影术, Demirjian牙龄, 诊断效能

Abstract:

Objective: To investigate the correlation between morphological stages of midpalatal suture (MPS) and Demirjian dental age.Methods: In this retrospective study, 1 076 cone-beam CT (CBCT) images (female: 675, male: 401; age range: 6.0 to 21.0 years) were included. Horizontal view of each sample’s CBCT images was observed, each sample’s MPS stage was recorded, and dental age. MPS stage and dental age were ordered with categorical variables. Therefore, their correlation was investigated through Spearman correlation coefficient analysis and diagnostic test analysis.Results: (1) For left lower second premolar: 95.2% of those in dental age stage B-D were in MPS 1-2, accounting for the largest proportion. 85.3% of those in dental age stage E were in MPS 1-2, still accounting for the largest proportion. Another 14.7% were in MPS 3. 45.1% of those in dental age stage F were in MPS 3, 46.1% in MPS 1-2, and another 8.8% in MPS 4s1. 49.8% of those in dental age stage G were in MPS 3. 24.9% in MPS 4s1, and 18.9% in MPS 1-2. 80.1% of those in dental age stage H were in MPS 4-5. Another 16.3% were in MPS 3. (2) For left lower second molar: 89.7% of those in dental age stage B-D were in MPS 1-2, accounting for the largest proportion. 67.4% of those in dental age stage E were in MPS 1-2, still accounting for the largest proportion. Another 26.1% were in MPS 3. 55.3% of those in dental age stage F were in MPS 3, 34.2% in MPS 1-2, and another 10.5% in MPS 4s1. 50.7% of those in dental age stage G were in MPS 3, 24.3% in MPS 4s1, and 16.8% in MPS 1-2. 83.8% of those in dental age stage H were in MPS 4-5, another 14.2% were in MPS 3. (3) To diagnose MPS stage with dental age, diagnostic pairs with good performance included: Dental age of left lower second molar-MPS: H-4s2, H-5, D-1; Dental age of left lower second premolar-MPS: H-4s2, H-5, G-3. Other diagnostic pairs were of ordinary diagnostic efficiency. (4) For dental age-MPS Spearman correlation analysis, dental age of left lower second molar-MPS had the highest Spearman coefficient (0.68), dental age of left lower second premolar-MPS was the second high (0.64). (5) Dental age stage H of left lower second molar or left lower second premolar indicated that the individual was later than MPS 4s2.Conclusion: Dental age’s diagnostic efficiency for MPS stage is ordinary on the whole, except for some pairs with good performance. Therefore, pre-treatment CBCT examination should be considered as assistance for evaluating maturation and fusion status of midpalatal suture.

Key words: Midpalatal suture, Cone-beam computed tomography, Demirjian dental age, Diagnostic efficiency

中图分类号: 

  • R783.5

表1

腭中缝形态特点分期与牙龄频数分布表"

MPS stage Dental age of left lower second premolar Dental age of left lower second molar
B C D E F G H Total B C D E F G H Total
1 1 1 13 21 29 30 11 106 1 3 37 16 18 27 4 106
2 0 0 5 8 18 17 13 61 0 0 11 15 8 18 9 61
3 0 0 1 5 46 124 109 285 0 0 6 12 42 136 89 285
4s1 0 0 0 0 6 62 195 263 0 0 0 2 8 65 188 263
4s2 0 0 0 0 2 14 261 277 0 0 0 1 0 18 258 277
5 0 0 0 0 1 2 81 84 0 0 0 0 0 4 80 84
Total 1 1 19 34 102 249 670 1 076 1 3 54 46 76 268 628 1 076

表2

腭中缝形态特点分期与牙龄的Spearman相关系数检验"

Items Left lower canine Left lower first premolar Left lower second premolar Left lower second molar
Spearman P value Spearman P value Spearman P value Spearman P value
Female 0.578 <0.001 0.557 <0.001 0.602 <0.001 0.667 <0.001
Male 0.668 <0.001 0.662 <0.001 0.676 <0.001 0.699 <0.001
Total 0.618 <0.001 0.605 <0.001 0.636 <0.001 0.683 <0.001

图1

腭中缝形态特点分期与牙龄诊断试验分析:ROC曲线"

表3

腭中缝形态特点分期与牙龄诊断试验分析:诊断效能指标"

Index Left lower canine Left lower first premolar
DA H-MPS 4s2 DA G-MPS 3 DA H-MPS 5 DA H-MPS 4s2 DA H-MPS 5 DA G-MPS 3
AUC 0.696 0.662 0.649 0.678 0.660 0.642
PLR 1.710 3.066 1.471 1.605 1.488 3.025
Sensitivity 0.946 0.481 0.929 0.946 0.976 0.425
Specificity 0.447 0.843 0.369 0.411 0.344 0.860
Index Left lower second premolar Left lower second molar
DA H-MPS 4s2 DA H-MPS 5 DA G-MPS 3 DA H-MPS 4s2 DA H-MPS 5 DA D-MPS 1
AUC 0.715 0.685 0.639 0.734 0.700 0.666
PLR 1.841 1.624 2.753 2.011 1.724 19.917
Sensitivity 0.942 0.964 0.435 0.931 0.952 0.349
Specificity 0.488 0.406 0.842 0.537 0.448 0.982
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