Figure 2 - available via license: Creative Commons Attribution 4.0 International
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Conventional radiography of the neck -Lateral view (and after the interpretation of the image -Extensive degenerative changes involving the C3, C4 and C5 vertebral bodies, as well as fusion of the apophyseal joints C2-C3 and C4-C5.
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Background: Rheumatoid arthritis (RA) may affect any diarthrodial joint with a predilection on the peripheral skeleton in a symmetrical manner. When the axial skeleton is affected, it is the cervical spine (CS) that gets involved with potentially detrimental effects, if not treated promptly.
Case: A 60-year-old female suffering from RA presented wi...
Contexts in source publication
Context 1
... tests revealed anaemia of chronic disease (ACD, Hb 9 g/dl, serum ferritin 158 mg/dl and serum ferrum 7 mg/dl), elevated acute phase reactants and high titres of RF and ACPA antibodies. Radiological evaluation of the CS disclosed extensive degenerative changes involving the C3, C4 and C5 vertebral bodies, as well as fusion of the apophyseal joints C2,C3 and C4,C5 (Figure 2). Magnetic resonance imaging (MRI) showed SAS and spinal cord compression at C3 level and to a less extent in other levels (Figure 3A,B). ...
Citations
... People commonly overlook neck pain as a common ailment caused by stress and work pressure. However, ignoring neck pain can have catastrophic effects on the cervical spine if not treated promptly [4]. Patients engaged in accidents may be in an unstable condition in some situations, demanding rapid medical attention and vigilant monitoring to avoid delays in critical procedures. ...
Cervical spine fractures are a medical emergency that can cause permanent paralysis and even death. Traditional fracture detection techniques, such as manual radiography image interpretation, are time-consuming and prone to human error. Deep learning algorithms have shown promising results in various medical imaging applications i.e., disease diagnosis, including fracture detection of bones. In this study, we propose a two-stage approach for detecting cervical spine fractures. The first stage employs a convolutional neural network (CNN) model to determine the presence or absence of a fracture in the cervical spine, using a dataset of cervical spine Computed Tomography (CT) scan images as well as Grad-CAM for enhanced visualization and interpretation. In the second stage, our focus shifts to specific vertebrae within the cervical spine. To accomplish this task, we trained and evaluated the performance of the YOLOv5 and YOLOv8 models with 9170 images consisting of seven vertebrae. The detection results of both YOLO versions are compared and evaluated. The precision, recall, mAP50, and mAP50-90 were 0.900, 0.890, 0.935, 0.872, respectively. The results of this research demonstrate the potential of deep learning-based approaches for cervical spine fracture detection. By automating the detection process, these algorithms can assist radiologists and healthcare professionals in making accurate and timely diagnoses, leading to improved patient outcomes.
Rheumatoid Arthritis (RA) is an autoimmune disease that is characterised by progressive joint disorder with significant pain and stiffness, which lead to functional disability and systemic complications if left untreated. The direct (health care costs) and indirect (productivity loss) socioeconomic costs of the disease are of major significance.
