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基于多个标志点的生长患者自动配准方法的评估。

Evaluation of an automated superimposition method based on multiple landmarks for growing patients.

出版信息

Angle Orthod. 2022 Mar 1;92(2):226-232. doi: 10.2319/010121-1.1.

Abstract

OBJECTIVES

To determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes.

MATERIALS AND METHODS

Serial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment. The T1 and T2 lateral images were manually traced. Three different superimposition methods: Björk's structural method, conventional SN, and the multiple landmark (ML) superimposition methods were applied. Bjork's structural method was used as the gold standard. Comparisons among the superimposition methods were carried out by measuring the linear distances between Anterior Nasal Spine, point A, point B, and Pogonion using each superimposition method. Multiple linear regression analysis was performed to identify factors that could affect the accuracy of the superimpositions.

RESULTS

The ML superimposition method demonstrated smaller differences from Björk's method than the conventional SN method did. Greater differences among the cephalometric landmarks tested resulted when: the designated point was farther from the cranial base, the T1 age was older, and the more time elapsed between T1 and T2.

CONCLUSIONS

From the results of this study in growing patients, the ML superimposition method seems to be more similar to Björk's structural method than the SN superimposition method. A major advantage of the ML method is likely to be that it can be applied automatically and may be just as reliable as manual superimposition methods.

摘要

目的

确定使用六个标志点(鞍结节、鼻根点、耳点、眶点、鼻底点和翼突点)的自动叠加方法是否比传统的 Sella-Nasion(SN)方法更适合评估生长变化。

材料和方法

对 268 名未接受正畸治疗的生长儿童进行了平均间隔 2.7 年的连续侧位头颅侧位片检查。T1 和 T2 侧位图像进行手动追踪。应用了三种不同的叠加方法:Björk 的结构方法、传统的 SN 和多标志点(ML)叠加方法。Björk 的结构方法被用作金标准。使用每种叠加方法测量前鼻棘、A 点、B 点和颏下点之间的线性距离,比较叠加方法。进行多元线性回归分析,以确定可能影响叠加准确性的因素。

结果

ML 叠加方法与 Björk 方法相比,与传统的 SN 方法相比,差异更小。当指定点离颅底越远、T1 年龄越大、T1 和 T2 之间的时间间隔越长时,这些头影测量标志点之间的差异越大。

结论

从这项生长患者的研究结果来看,ML 叠加方法似乎比 SN 叠加方法更类似于 Björk 的结构方法。ML 方法的一个主要优点可能是它可以自动应用,并且可能与手动叠加方法一样可靠。

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