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一种基于体积的新型锥形束计算机断层扫描根尖指数。

A Novel Volume-based Cone-beam Computed Tomographic Periapical Index.

作者信息

Boubaris Matthew, Chan Keen Long, Zhao Wei, Cameron Andrew, Sun Jing, Love Robert, George Roy

机构信息

School of Dentistry and Oral Health, Griffith University, Gold Coast, Australia.

Menzies Health Institute Queensland and School of Medicine, Griffith University, Gold Coast, Australia.

出版信息

J Endod. 2021 Aug;47(8):1308-1313. doi: 10.1016/j.joen.2021.04.027. Epub 2021 May 10.

Abstract

INTRODUCTION

The purpose of this study was to evaluate the variations in the volume of periapical lesions scored using a cone-beam computed tomographic periapical index (CBCTPAI) and to develop a new volume-based periapical index.

METHODS

Cone-beam computed tomographic images were obtained from InteleViewer (Intelerad Medical Systems Incorporated, Montreal, Canada). Teeth with a periapical radiolucency or with a history of endodontic treatment were included in this study. Using 3-dimensional medical imaging processing software (Mimics Research; Materialise NV, Leuven, Belgium), the maximum diameter of 273 periapical lesions and their corresponding CBCTPAI score was determined. The software was then used to determine the volume of the lesions using a semiautomatic segmentation technique.

RESULTS

There was a substantial variation in the volume for CBCTPAI scores 3, 4, and 5, which was demonstrated by the variance and range, thus making it difficult to use the current CBCTPAI as a method to predict volume and treatment outcomes. A new index, the cone-beam computed tomographic periapical volume index (CBCTPAVI), was developed using partition classification analysis. The results for the new index demonstrated high levels of sensitivity, specificity, precision, and area under the curve, all at 0.90 or more, except 1 sensitivity for CBCTPAVI 1 at 0.875. Overall, the accurate classification rate was 98.169%, and the root mean square error rate was low at 0.07.

CONCLUSIONS

The proposed CBCTPAVI will allow clinicians to classify lesions based on their true 3-dimensional size, accurately assess healing of lesions, and predict treatment outcomes.

摘要

引言

本研究的目的是评估使用锥形束计算机断层扫描根尖指数(CBCTPAI)对根尖周病变体积评分的差异,并开发一种基于体积的新根尖指数。

方法

从InteleViewer(加拿大蒙特利尔的Intelerad医疗系统公司)获取锥形束计算机断层扫描图像。本研究纳入有根尖周透射区或有牙髓治疗史的牙齿。使用三维医学成像处理软件(Mimics Research;比利时鲁汶的Materialise NV公司)确定273个根尖周病变的最大直径及其相应的CBCTPAI评分。然后使用该软件通过半自动分割技术确定病变的体积。

结果

CBCTPAI评分为3、4和5时,体积存在显著差异,这通过方差和范围得到证明,因此难以将当前的CBCTPAI用作预测体积和治疗结果的方法。使用分区分类分析开发了一种新的指数,即锥形束计算机断层扫描根尖周体积指数(CBCTPAVI)。新指数的结果显示出高水平的敏感性、特异性、精确性和曲线下面积,除了CBCTPAVI 1的敏感性为0.875外,其他均在0.90或更高。总体而言,准确分类率为98.169%,均方根误差率较低,为0.07。

结论

所提出的CBCTPAVI将使临床医生能够根据病变的真实三维大小对病变进行分类,准确评估病变的愈合情况,并预测治疗结果。

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