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. 2022;33(1):93-101.
doi: 10.52312/jdrs.2022.445. Epub 2022 Mar 28.

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

Yüksel Maraş  1 , Gül Tokdemir, Kemal Üreten, Ebru Atalar, Semra Duran, Hakan Maraş
Affiliations

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods

Yüksel Maraş et al. Jt Dis Relat Surg. 2022.

Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

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Conflict of interest statement

Conflict of Interest: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

Figures

Figure 1
Figure 1. Image samples from the data set.
Figure 2
Figure 2. VGG-16 Deep Transfer learning training accuracy (upper image), training loss (lower image). VGG: Visual geometry group.
Figure 3
Figure 3. VGG-19 Deep Transfer learning training accuracy (upper image), training loss (lower image). VGG: Visual geometry group.
Figure 4
Figure 4. ResNet Deep Transfer learning training accuracy (upper image), training loss (lower image).
Figure 5
Figure 5. DenseNet Deep Transfer learning training accuracy (upper image), training loss (lower image).
Figure 6
Figure 6. Confusion matrices for deep transfer learning using VGG-16, VGG-19, ResNet-101, DenseNet-201. VGG: Visual geometry group.
See this image and copyright information in PMC

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