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智能手机应用追踪、基于网络的人工智能追踪与传统人工追踪方法的评估与比较。

Evaluation and comparison of smartphone application tracing, web based artificial intelligence tracing and conventional hand tracing methods.

机构信息

Private Practice, Istanbul, Turkey.

Department of Orthodontics, Faculty of Dentistry, Istanbul Aydin University, Istanbul, Turkey.

出版信息

J Stomatol Oral Maxillofac Surg. 2022 Nov;123(6):e906-e915. doi: 10.1016/j.jormas.2022.07.017. Epub 2022 Jul 26.

DOI:10.1016/j.jormas.2022.07.017
PMID:35901950
Abstract

AIM

The aim of this study was to compare and evaluate the reliability of three different cephalometric assessment methods: Smartphone Application Tracing Method CephNinja (SATM), Web Based Artificial Intelligence (AI) Driven Tracing Method WebCeph (WATM) and Conventional Hand Tracing Method (CHTM).

METHODS

110 lateral cephalometric radiographs were enrolled in the study and 4 linear and 7 angular parameters were traced and measured by one examiner using CephNinja, WebCeph and conventional hand tracing methods. Independent-samples Kolmogorov-Smirnov and Shapiro Wilks tests were used to compare the mean values of intra-examiner differences. Both intra-method and inter-method correlations were evaluated.

RESULTS

There were statistically significant differences between the methods in terms of SNA (p:0.003; p < 0.05); SNB measurements (p:0.001; p < 0.05); SN-MP angle (p:0.001; p < 0.05); U1-SN angle (p:0.001; p < 0.05); L1-NB(mm) (p:0.007; p < 0.05) and E Line-Upper Lip(mm) measurements (p:0.013; p < 0.05). All intra-method correlation coefficients are 80% and above. In terms of inter-method coefficients the lowest coefficient of agreement is 0.170 and it is the coefficient of agreement between CHTM and SATM for measurement of U1-NA. The highest coefficient of agreement is 0.884 which is between WBTM and SATM for SNB measurement.

CONCLUSIONS

The null hypothesis was rejected. There were statistically and clinically significant differences in various measurements among groups.

摘要

目的

本研究旨在比较和评估三种不同的头影测量评估方法的可靠性:智能手机应用追踪法 CephNinja(SATM)、基于网络的人工智能(AI)驱动追踪法 WebCeph(WATM)和传统手工追踪法(CHTM)。

方法

本研究纳入了 110 张侧位头颅侧位片,由同一位检查者使用 CephNinja、WebCeph 和传统手工追踪方法分别追踪和测量 4 项线性和 7 项角度参数。使用独立样本 Kolmogorov-Smirnov 和 Shapiro-Wilk 检验比较组内差异的均值。评估了内-间方法的相关性。

结果

在 SNA(p:0.003;p<0.05)、SNB 测量值(p:0.001;p<0.05)、SN-MP 角(p:0.001;p<0.05)、U1-SN 角(p:0.001;p<0.05)、L1-NB(mm)(p:0.007;p<0.05)和 E 线-上唇(mm)测量值(p:0.013;p<0.05)方面,各方法之间存在统计学显著差异。所有内-间方法的相关系数均在 80%以上。就间方法系数而言,一致性最低的系数为 0.170,这是 CHTM 和 SATM 测量 U1-NA 的一致性系数。一致性最高的系数为 0.884,这是 WBTM 和 SATM 测量 SNB 的一致性系数。

结论

零假设被拒绝。各组间的各种测量值存在统计学和临床显著差异。

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