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从基于深度学习的X射线图像中识别结根下第二的根运河形态

Weiwei Wu¹, Surong Chen¹, Pan Chen¹

¹Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Journal of endodontics

|May 31, 2024

概括

深度学习模型,特别是卷积神经网络 (CNN),从二维X射线中准确地识别了2D根底下第二牙中的3D根管形态. 这些人工智能模型在分类准确性方面表现优于内 residents.

科学领域:

牙周内科医院牙周内科医院牙周内科医院
人工智能的人工智能
医疗成像医学成像

背景情况:

精确的3D根管形态的鉴定在融合根的下第二 (MSMs) 中对于成功的根管治疗至关重要.
传统的二维成像方法往往缺乏复杂根结构所需的细节.
深度学习提供了一种新的方法来分析医疗图像中的复杂解剖数据.

研究的目的:

通过使用二维X射线图像,评估深度学习模型在识别MSM的3D根管形态方面的有效性.
将卷积神经网络 (CNN) 的诊断性能与内牙科居民进行比较.

主要方法:

用微型计算机断层扫描 (CT) 分析了271个融合根的MSM的数据集,以获得3D地面真相.
使用传统技术和多角度投影方法来增强二维X射线图像.
预先训练的CNN (VGG19,ResNet18,ResNet50,EfficientNet-b5) 被用来进行形态分类.

主要成果:

与传统增强相比,多角度投影方法总体上提高了CNN的性能.
采用多角度投影的ResNet18获得了最高的整体精度 (79.25%),用于合并 (81.13%),对称 (86.79%) 和不对称 (90.57%) 类型的特定精度.
与内牙科住院医生 (平均60.38%) 相比,CNN的分类准确度显著更高 (平均79.25%).

结论:

卷积神经网络在从二维X射线图像中识别MSM的3D根管形态方面非常有效.
深度学习模型显示出与人类专家 (内牙科住院医生) 相比,更高的诊断能力.
CNN可以有效地利用二维成像数据来帮助牙周内科复杂的三维诊断.

关键词:

卷积神经网络是一种卷积神经网络.在X射线成像中使用X射线成像.深度学习是一种深度学习.化根的下第二牙.微型计算机断层扫描技术

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