Ankara Şehir Hastanesi, Romatoloji Kliniği, 06800 Çankaya, Ankara, Türkiye.
Jt Dis Relat Surg. 2022;33(1):93-101. doi: 10.52312/jdrs.2022.445. Epub 2022 Mar 28.
In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology.
In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease.
We compared the performances of the classification models in terms of performance metrics such as accuracy, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results.
The results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs.
本研究旨在利用深度卷积神经网络（DCNN）技术区分正常颈椎图谱和引起机械性颈部疼痛的疾病图谱。
本回顾性研究使用卷积神经网络，并应用迁移学习方法，使用预训练的 VGG-16、VGG-19、Resnet-101 和 DenseNet-201 网络。我们的数据集中包括 161 例正常颈椎侧位片和 170 例伴骨关节炎和颈椎退行性椎间盘病的颈椎侧位片。
我们比较了分类模型在准确性、敏感性、特异性和精度等性能指标方面的性能。在准确性（93.9%）、敏感性（95.8%）、特异性（92.0%）和精度（92.0%）方面，预训练的 VGG-16 网络的表现优于其他模型。
本研究结果表明，深度学习方法是一种有前途的支持工具，可用于使用 DCNN 自动控制颈椎图谱，并排除正常图谱。这样的支持工具可以减少诊断时间，并为放射科医生或临床医生提供更多时间来解释异常图谱。
