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首页> 外文期刊>European journal of oral sciences >Latent variable approach to correct errors in radiographic measurements.
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Latent variable approach to correct errors in radiographic measurements.

机译:潜在变量方法可纠正射线照相测量中的误差。

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摘要

Radiographic outcomes are important for the diagnosis and treatment of periodontal diseases. However, the assessment of radiographic measurements is affected by many factors, and it is therefore difficult to ascertain changes in radiographic outcomes. In this study, we proposed a latent variable approach to correct for the distortion in the radiographic measurements in pairs of periapical radiographs taken before and after periodontal treatment. Clinical data from 123 patients treated with non-surgical periodontal therapy was used to illustrate the latent variable approach in assessing radiographic changes in infrabony defect depth. Results were compared with a correction factor method. Computer simulations were also undertaken to evaluate the performance of these two methods compared with uncorrected, raw measurements by calculating their intraclass correlation coefficients (ICCs). The example data set showed that the latent variable method (LVM) and the correction factor method (CFM) were comparable. Simulations showed that both methods achieved very high ICCs in different scenarios, whilst uncorrected raw measurements had relatively low ICCs. This study suggests that correction for errors in radiographic measurements is required for routine radiographs. Whilst both LVM and CFM achieve excellent results, LVM is more flexible in handling missing values, and may provide better results when treatment effects are large.
机译:放射学结果对于牙周疾病的诊断和治疗很重要。但是,放射线测量的评估受到许多因素的影响,因此很难确定放射线结果的变化。在这项研究中,我们提出了一种潜在的变量方法,以校正牙周治疗前后在成对的根尖X线照片中射线照相测量中的变形。来自123例接受非手术牙周治疗的患者的临床数据用于说明潜在变量方法,以评估骨缺损深度的影像学变化。将结果与校正因子方法进行比较。通过计算类内相关系数(ICC),还进行了计算机模拟以评估这两种方法与未经校正的原始测量结果的性能。示例数据集显示,潜在变量方法(LVM)和校正因子方法(CFM)是可比的。仿真表明,这两种方法在不同的情况下均实现了很高的ICC,而未经校正的原始测量结果具有相对较低的ICC。这项研究表明常规放射线照相需要校正放射线照相测量中的误差。虽然LVM和CFM均能获得出色的结果,但LVM在处理缺失值时更加灵活,并且在治疗效果大时可能会提供更好的结果。

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