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

Abstract

Although the cervical spine supports the head, it has quite functional features such as flexion, extension, lateral flexion, and rotation. Due to this wide range of motion, there is a susceptibility to trauma and degenerative changes. Neck pain due to cervical spine problems is a very common medical problem in the adult population. Cervical strain, cervical spondylosis, and discopathies are common causes of mechanical neck pain. Apart from these, traumas, tumors and reflected pain are also important causes of neck pain. [1] Patients with non-traumatic neck pain can be examined in various polyclinics such as family medicine, internal medicine, orthopedics, physical medicine and rehabilitation, neurology, neurosurgery, and rheumatology outpatient clinics. Almost every patient is evaluated with plain radiography in terms 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.

Mid-sagittal cross-section identification for vertebra landmarking in MR spine images

“…For localization tasks, DSNT was used as the top level. Maraş et al 9 compared different deep convolutional neural networks (DCNN) in terms of their performance for vertebral segmentation of the X-ray images of the cervical spine. The pre-trained networks used in this work were VGG-16, VGG-19, Resnet-101 and DenseNet-201, with the overall achieved classification accuracy of 93.9, 91.8, 89.8 and 85.7%, respectively.…”

Artificial intelligence, machine learning, and deep learning in orthopedic surgery

“…The CNN is an artificial neural network (ANN) algorithm that applies multilayer neural network structure. [2] Deep learning may be a beneficial assistant in the detection of bone fractures, for the evaluation of…”

“…President, Turkish Joint Diseases Foundation, Ankara, Türkiye cervical arthrosis and robotic joint arthroplasty. [1][2][3] However, the public unawareness of the doubtful outcome superiority associated with robotic-assisted orthopedic surgery may contribute to misinformed decisions in some patients. For a better evaluation of the utility of robotic joint arthroplasty, further well-designed, prospective, controlled studies with long-term follow-up would be helpful.…”

“…The CNN is an artificial neural network (ANN) algorithm that applies multilayer neural network structure. [ 2 ]…”

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods

“…Alexnet, VGG-16, GoogLeNet, ResNet-50, and Squeezenet are the most well-known transfer learning methods. [6][7][8] Research has shown that deep learning methods are very effective in medical image analysis by saving time and computational costs. [5] In deep learning methods, the working mechanisms of neurons in the human brain are imitated, together with simultaneous utilization of the processing power of the computer.…”