■实验研究。
■本研究旨在研究人工神经网络(ANN)在使用KonstanzInformationMiner(KNIME)分析平台检测齿状突骨折中的潜在用途,该平台提供了一种使用X线成像进行计算机辅助诊断的技术。
■在医学图像处理中,利用X线摄影成像的ANN进行计算机辅助诊断正变得越来越流行.齿状突骨折是一种常见的轴骨折,占所有颈椎骨折的10%-15%。然而,尚未对使用ANN的计算机辅助诊断进行文献综述.
■这项研究分析了从数据集存储库中获得的432张张口(齿状突)颈椎X射线图像的射线照相视图,用于基于卷积神经网络理论开发神经网络模型。所有图像都包含诊断信息,包括216个正常齿状突个体的射线照相图像和216个急性齿状突骨折患者的图像。该模型将每个图像分类为显示齿状突骨折或不显示齿状突骨折。具体来说,70%的图像是用于模型训练的训练数据集,30%用于测试。KNIME的基于图形用户界面的编程启用了类标签注释,数据预处理,模型训练,和绩效评估。
■KNIME的图形用户界面程序用于报告所有放射摄影X射线成像特征。ANN模型进行了50个时期的训练。检测齿状突骨折的性能指标包括敏感性,特异性,F-measure,预测误差为100%,95.4%,97.77%,和2.3%,分别。模型的准确性占接收器工作特征曲线下面积的97%,用于诊断齿状突骨折。
■具有KNIME分析平台的ANN模型已成功用于使用X线图像对齿状突骨折进行计算机辅助诊断。这种方法可以帮助放射科医生进行筛查,检测,和急性齿状突骨折的诊断。
UNASSIGNED: An experimental study.
UNASSIGNED: This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging.
UNASSIGNED: In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%-15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.
UNASSIGNED: This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME\'s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.
UNASSIGNED: The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model\'s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.
UNASSIGNED: The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.