构建和评估基于人工智能的拔牙 CBCT 分辨率优化技术。

IF 3.5 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
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引用次数: 0

摘要

导言:在牙科临床实践中,锥形束计算机断层扫描(CBCT)常用来帮助医生识别根管系统的复杂形态。但由于其分辨率的限制,某些细小的解剖结构仍无法通过 CBCT 准确识别。本研究的目的是在深度学习模型的帮助下,对拔出的人类牙齿的CBCT图像进行图像超分辨率(SR)处理,并通过三维重建比较CBCT、SRCT和显微计算机断层扫描(Micro-CT)图像之间的差异:方法:选择并修改了深度学习模型(Basicvsr++)。方法:选择并修改了深度学习模型(Basicvsr++)。数据集由符合纳入标准的 171 颗拔牙组成,其中 40 颗上颌第一磨牙作为训练集,40 颗上颌第一磨牙以及 91 颗其他位置的牙齿作为外部测试集。使用 Mimics Research 17.0 重建测试集中每颗牙齿的相应 CBCT、SRCT 和 Micro-CT 图像,并记录三组的根管识别率。测量的参数包括:硬组织体积(V1)、髓室和根管系统体积(V2)、孔下可见根管长度(VL-X,其中 X 代表特定根管)、根管冠状轴与牙齿长轴的交角(∠X,其中 X 代表特定根管)。使用配对样本 t 检验和 Wilcoxon 检验分析对 CBCT 和 SRCT 图像之间的数据进行统计分析,并将 Micro-CT 图像的测量结果作为金标准:结果:所有测试牙齿的图像均已成功通过超分辨率程序处理。在上颌第一磨牙的 4 个腭窦中,CBCT 组 MB2 的识别率为 72%(18/25),SRCT 组为 92%(23/25),Micro-CT 组为 100%(25/25)。在所有受测牙齿中,SRCT 与 Micro-CT 之间的硬组织体积差异明显小于 CBCT 与 Micro-CT 之间的差异(P < 0.05),下颌第一磨牙除外(4-canal)。所有受测牙齿的髓腔和根管系统的体积也得到了类似的结果(P < 0.05)。在大多数根管中,SRCT 与 Micro-CT 之间的差异明显小于 CBCT 与 Micro-CT 之间的差异(P < 0.05):本研究开发的深度学习模型有助于优化 CBCT 拔牙根管形态。结论:本研究开发的深度学习模型有助于优化 CBCT 拔牙根管形态,并有助于识别上颌第一磨牙的 MB2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction and Evaluation of an AI-based CBCT Resolution Optimization Technique for Extracted Teeth

Introduction

In dental clinical practice, cone-beam computed tomography (CBCT) is commonly used to assist practitioners to recognize the complex morphology of root canal systems; however, because of its resolution limitations, certain small anatomical structures still cannot be accurately recognized on CBCT. The purpose of this study was to perform image super-resolution (SR) processing on CBCT images of extracted human teeth with the help of a deep learning model, and to compare the differences among CBCT, super-resolution computed tomography (SRCT), and micro-computed tomography (Micro-CT) images through three-dimensional reconstruction.

Methods

The deep learning model (Basicvsr++) was selected and modified. The dataset consisted of 171 extracted teeth that met inclusion criteria, with 40 maxillary first molars as the training set and 40 maxillary first molars as well as 91 teeth from other tooth positions as the external test set. The corresponding CBCT, SRCT, and Micro-CT images of each tooth in test sets were reconstructed using Mimics Research 17.0, and the root canal recognition rates in the 3 groups were recorded. The following parameters were measured: volume of hard tissue (V1), volume of pulp chamber and root canal system (V2), length of visible root canals under orifice (VL-X, where X represents the specific root canal), and intersection angle between coronal axis of canal and long axis of tooth (∠X, where X represents the specific root canal). Data were statistically analyzed between CBCT and SRCT images using paired sample t-test and Wilcoxon test analysis, with the measurement from Micro-CT images as the gold standard.

Results

Images from all tested teeth were successfully processed with the SR program. In 4-canal maxillary first molar, identification of MB2 was 72% (18/25) in CBCT group, 92% (23/25) in SRCT group, and 100% (25/25) in Micro-CT group. The difference of hard tissue volume between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT in all tested teeth except 4-canal mandibular first molar (P < .05). Similar results were obtained in volume of pulp chamber and root canal system in all tested teeth (P < .05). As for length of visible root canals under orifice, the difference between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT (P < .05) in most root canals.

Conclusions

The deep learning model developed in this study helps to optimize the root canal morphology of extracted teeth in CBCT. And it may be helpful for the identification of MB2 in the maxillary first molar.

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来源期刊
Journal of endodontics
Journal of endodontics 医学-牙科与口腔外科
CiteScore
8.80
自引率
9.50%
发文量
224
审稿时长
42 days
期刊介绍: The Journal of Endodontics, the official journal of the American Association of Endodontists, publishes scientific articles, case reports and comparison studies evaluating materials and methods of pulp conservation and endodontic treatment. Endodontists and general dentists can learn about new concepts in root canal treatment and the latest advances in techniques and instrumentation in the one journal that helps them keep pace with rapid changes in this field.
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