头部定位误差对全自动人工智能头颅测量软件准确性的影响

IF 3.2
Alessandro Polizzi, Antonino Lo Giudice, Cristina Conforte, Gaetano Isola, Rosalia Leonardi
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引用次数: 0

摘要

目的:评估三种全自动软件系统与非自动化头颅测量分析软件的准确性,这些软件使用正确和不正确的头部位置的脑电图进行测试。材料和方法:研究样本包括从一个较大的预处理正畸记录池中回顾性检索的40张侧位脑电图。采集头颅图像,分为正确头位组(CHP)和错误头位组(IHP)。通过人工标记(Dolphin软件)作为参考,以及全自动人工智能软件(WebCeph、Ceph Assistant和AudaxCeph)获得头颅测量数据。组内比较使用类内相关系数(ICC)和配对t检验,而基于人工智能(AI)的软件应用程序之间的性能比较使用方差分析和事后分析。结果:测试的软件表现出良好的水平一致性的角度测量,而线性测量更容易出错。AudaxCeph显示出最一致的准确性,在几个骨骼参数上实现了极好的一致性(ICC > 0.90);然而,它不能准确地检测软组织。WebCeph和Ceph Assistant表现出更大的可变性,特别是线性测量(ICC < 0.50)。位置误差大大降低了测量精度,Go-Me等线性参数在所有软件中表现出最差的一致性。结论:基于人工智能的头颅测量软件显示出不同的准确性,这取决于头颅测量结果,并且在涉及头颅成像位置错误的情况下,这种模式会加剧。因此,专家临床医生的监督仍然需要最小化边际误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Influence of head positioning errors on the accuracy of fully automated artificial intelligence-based cephalometric software.

Objectives: To evaluate the accuracy of three fully automated software systems compared to nonautomated cephalometric analysis software tested using cephalograms featuring correct and incorrect head positions.

Materials and methods: The study sample consisted of 40 lateral cephalograms retrieved retrospectively from a larger pool of pretreatment orthodontic records. Cephalograms were recruited and divided into correct head posture group (CHP) and incorrect head posture group (IHP). Cephalometric data were obtained by manual landmarking (Dolphin software), which served as a reference, and by fully automated AI software (WebCeph, Ceph Assistant, and AudaxCeph). Intraclass correlation coefficients (ICC) and paired t-tests were used for intragroup comparisons, whereas analysis of variance and post-hoc analysis were used to compare performance among artificial intelligence (AI) based software applications.

Results: The tested software exhibited a good level of consistency for angular measurements whereas linear measurements were more error-prone. AudaxCeph demonstrated the most consistent accuracy, achieving excellent agreement (ICC > 0.90) for several skeletal parameters; however, it failed in detecting soft tissue accurately. WebCeph and Ceph Assistant showed greater variability, especially for linear measurements (ICC < 0.50). Positional errors drastically reduced measurement accuracy, with linear parameters such as Go-Me showing the poorest agreement across all software.

Conclusions: AI-based cephalometric software demonstrated variable accuracy depending on the cephalometric measurement, and this pattern was exacerbated under conditions involving positional errors in cephalograms. Accordingly, oversight by expert clinicians is still required to minimize marginal error.

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