Spine X-ray Research Articles

Automatic segmentation of spine x-ray images based on multiscale feature enhancement network.

Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications. In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61±0.431 for MIoU, 92.42±0.329 for DSC, and 99.51±0.037 for Global_accuracy. Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.

Artificial neural networks for the detection of odontoid fractures using the Konstanz Information Miner Analytics Platform.

An experimental study. 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. 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. 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. 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. 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.

A high-quality dataset featuring classified and annotated cervical spine X-ray atlas

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

Evaluation of the Possibility of Axial Psoriatic Arthritis Patients Meeting Classification Criteria for Axial Spondyloarthritis and Ankylosing Spondylitis.

The objective of the study was to analyze whether axial psoriatic arthritis (axPsA) patients meet classification criteria for axial spondyloarthritis (axSpA) and ankylosing spondylitis (AS). A total of 104patients (66 men and 38 women) with PsA according to CASPAR criteria were examined, all patients had back pain. Patients were evaluated for presence of inflammatory back pain (IBP) by ASAS criteria. Back pain not meeting the ASAS criteria was taken to be chronic back pain (chrBP). Patients underwent hands, feet and pelvis, cervical spine and lumbar spine X-rays. Erosions, osteolysis, and juxta-articular new bone formation were evaluated. Definite radiographic sacroiliitis (d-rSI) was defined as bilateral grade ≥ 2 or unilateral grade ≥ 3. Nineteen patients without d-rSI underwent sacroiliac joints MRI. Ninety-three patients underwent HLA B27 examination. The number of patients who met the criteria for axSpA (ASAS) and the modified New York (mNY) criteria for AS was determined. IBP was identified in 67 (64.4%) patients; chrBP, in 37 (35.6%) patients; 31 (29.8%) patient were of older age (over 40) at the onset of IBP/chrBP; 57 (58.8%) patients had d-rSI; 6 (31.6%) patients had MRI-SI; syndesmophytes were detected in 57 (58.8%) cases. Among 40 patients without d-rSI, 19 (47.5%) had syndesmophytes. In 38/97(39.2%) patients d-rSI was detected along with syndesmophytes, while 19/97(19.6%) patients had isolated d-rSI without spondylitis, and 19/97(19.6%) patients had isolated syndesmophytes without d-rSI. HLA B27 was present in 28 (30.1%) cases. 51 (55.4%) patients met criteria for axSpA. Forty-one (44.6%) patients did not meet criteria for axSpA; however, 27 (65.9%) of them had syndesmophytes. Forty-eight (48.5%) PsA patients met mNY criteria for AS. Among these patients, a set of specific features was revealed: 18 (37.5%) had no IBP, 18 (37.5%) were of older age (over 40) at the onset of IBP/chrBP, 34 (70.8%) had dactylitis, 38 (79.2%) had erosive polyarthritis, 23 (48.8%) had juxta-articular new bone formation, 14 (30.2%) had osteolysis, 23 (48.9%) had "chunky" non-marginal syndesmophytes, and 40 (82.6%) had nail psoriasis; 28 (66.6%) patients were HLA-B27 negative. Forty-five percent of axPsA patients do not meet criteria for axSpA. Characteristic features have been identified to differentiate axPsA from AS.

Identification of Spinal Disorders Based on Spine X-ray Dataset Processing Using the LBP and CNN Algorithms

This research will use deep learning in conducting spinal x-ray image analysis but computational time problems are a problem of this study. Computations on deep learning across multiple nodes can increase training time and longer computation time compared to machine learning models. Based on experimental results, the best spine x-ray image classification results when using the CNN model with accuracy at the training stage, evaluation stage and test stage were 69.00%, 83.33% and 81.16% respectively. CNN models optimized with LBP get the lowest accuracy, with results at the training stage of 62.64%, validation stage of 75.00% and testing stage of 65.22%. LBP feature extraction turns out to have several drawbacks when combined with the CNN model, one major drawback is its inability to process global spatial information while retaining local texture information which causes LBP to be unable to capture the entire structure or context of the image, focusing only on local patterns so that many features of the image are lost. Another issue is the sensitivity of CNNs to image data, which can affect classification accuracy.

An investigation into the chiropractic practice and communication of routine repetitive radiographic imaging for the location of postural misalignments.

Many clinicians use radiological imaging in efforts to locate and diagnose the cause of their patient's pain, relying on X-rays as a leading tool in clinical evaluation. This is fundamentally flawed because an X-ray represents a "snapshot" of the structural appearance of the spine and gives no indication of the current function of the spine. The health and well-being of any system, including the spinal motion segments, depend on the inter-relationship between structure and function. Pain, tissue damage, and injury are not always directly correlated. Due to such a high incidence of abnormalities found in asymptomatic patients, the diagnostic validity of X-rays can be questioned, especially when used in isolation of history and/or proper clinical assessment. The utility of routine X-rays is, therefore, questionable. One may posit that their application promotes overdiagnosis, and unvalidated treatment of X-ray findings (such as changes in postural curvature), which may mislead patients into believing these changes are directly responsible for their pain. A substantial amount of research has shown that there is no association between pain and reversed cervical curves. Accuracy can also be questioned, as X-ray measurements can vary based on the patient's standing position, which research shows is influenced by an overwhelming number of factors, such as patient positioning, patient physical and morphological changes over time, doctor interreliability, stress, pain, the patient's previous night's sleep or physical activity, hydration, and/or emotional state. Furthermore, research has concluded that strong evidence links various potential harms with routine, repeated X-rays, such as altered treatment procedures, overdiagnosis, radiation exposure, and unnecessary costs. Over the past two decades, medical boards and health associations worldwide have made a substantial effort to communicate better "when" imaging is required, with most education around reducing radiographic imaging. In this review, we describe concerns relating to the high-frequency, routine use of spinal X-rays in the primary care setting for spine-related pain in the absence of red-flag clinical signs.

Temporal Trends in Spinal Imaging in Ontario (2002-2019) and Manitoba (2001-2011), Canada.

Background Several studies have reported the overuse of spinal imaging, which, in Canada, led to several provincial pathways aimed at optimizing the use of imaging. We assessed temporal trends in spine imaging in two Canadian provinces. Methods We explored the use of X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) examinations of the cervical, thoracic, and lumbar spine regions among adults in Ontario (April 1, 2002, to March 31, 2019) and in Manitoba, Canada (April 1, 2001, to March 31, 2011) using linked Ontario Health Insurance Plan administrative databases and data from Manitoba Health. We calculated the age- and sex-adjusted rates of spinal X-ray, CT, and MRI examinations by dividing the number of imaging studies by the population of each province for each year and estimated the use of each imaging modality per 100,000 persons. Results The total cost of spine imaging in Ontario increased from $45.8 million in 2002/03 to $70.3 million in 2018/19 (a 54% increase), and in Manitoba from $2.2 million in 2001/02 to $5 million in 2010/11 (a 127% increase). In Ontario, rates of spine X-rays decreased by 12% and spine CT scans decreased by 28% over this time period, while in Manitoba, rates of spine X-rays and CT scans remained constant. Age- and sex-adjusted utilization of spinal MRI scans per 100,000 persons markedly increased over time in both Ontario (277%) and Manitoba (350%). Conclusion Despite efforts to reduce the use of inappropriate spinal imaging, both Ontario and Manitoba have greatly increased utilization of spine MRI in the past two decades.

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.

The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.

Extraarticular manifestations of juvenile idiopathic arthritis and their impact on health-related quality of life.

The objective of this study is to investigate extraarticular manifestations (EAMs) in patients with juvenile idiopathic arthritis (JIA) and assess their impact on health-related quality of life (HRQoL) among these patients. This cross-sectional analytic study was carried out on 117 patients with JIA. EAMs were identified clinically by history and examination. Sicca symptoms, peripheral neuropathy, enthesitis, and skin lesions were picked up during clinical examination. Pulmonary involvement was evaluated by high-resolution CT chest. Patients were assessed by abdominal ultrasonography to assess the size of liver and spleen. Atlantoaxial subluxation was evaluated by cervical spine x-rays. Patients were evaluated by Pediatric Quality of Life Inventory-4 (PedsQL-4) and PedsQL-3 arthritis module. The median age of patients was 14years with a median disease duration 4years, 82.9% were females. Of the studied 117 JIA patients, 85 patients (72.6%) had at least one EAM. Persistent fatigue (51.3%) was the most prevalent EAM, followed by recurrent skin rash (16.2%), enthesitis (15.4%), recurrent fever (13.7%), and uveitis (12%). Patients with EAMs scored significantly lower in physical functioning (p = 0.001), emotional functioning (p < 0.001), social functioning (p = 0.005), and school functioning (p = 0.001). Regarding PedsQL arthritis module, patients with EAM had also significantly lower scores than did patients without EAM on the domains of pain and hurt (p < 0.001), daily activities (p = 0.008), and worry (p = 0.001). EAMs are prevalent among JIA patients and have a negative impact on their HRQoL. So, early identification and treatment are highly recommended. Key Points • A large percentage of JIA patients experienced at least one extraarticular manifestation (EAM). • Persistent fatigue and recurrent skin rash are the most prevalent EAMs in JIA patients. • JIA patients with EAMs have worse scores in almost all domains of HRQoL.

#176 System to detect vascular calcification through convolutional neural networks

Abstract Background and Aims Cardiovascular disease is the major cause of death in patients with chronic kidney disease (CKD). It is increasingly believed that the presence of vascular calcification (VC) increases the risk of mortality due to cardiovascular events. VC is very common in CKD, especially in hemodialysis patient. To improve the life expectancy of patients, it is important to detect VC in the early stages of the disease. There are several ways to discover VC such as computed tomography (CT) scans, echocardiography, and X-Ray among others. However, one of the best options is the usage of X-ray images because is an affordable and simpler process in comparison to using CT scans or echocardiography. However, the problem with X-ray images is the difficulty of finding a well-trained professional that can detect the VC after analyzing the images which leads to possible misleading diagnoses. The goal of this study is to develop a prototype that detects Vascular Calcification from X-ray images in an automated way using the power of Convolutional Neural Networks (CNN). Method To train a CNN to detect VC from lumbosacral spine X-ray images, a set of 700 DICOM files complemented with 236 JPEG images was available. After getting the images, they had to be anonymized, and data, such as name and age was removed from each image. Then, the images were divided into three datasets: Train, Test, and Validation. Given that the dataset was not balanced between the pictures with VC (Positive) and those with no VC (Negative), it had to be reduced (Table 1). The next step was implemented to clean the images and prepare them to feed the CNN. An example of preprocessing is that some X-ray images were of the entire body. For the analysis, the VC detection was assessed at the anterior and the posterior walls of the abdominal aorta adjacent to vertebrae L1-L4. In addition to that, the DICOM images were converted to JPEG. After having the dataset prepared, a CNN architecture had to be selected. To achieve it, a revision of modern CNN architectures was performed. Two items were considered when selecting these: the performance on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and if there was an available pre-trained model. With that criteria, pre-trained EfficientNet-B0 and ResNet50 models were used to build the prototype due to the limited dataset. Results After having the dataset prepared, the architectures selected, and the pre-trained models, the Training, Validation and Test stages were executed. For Training, with EfficientNet-B0, the Accuracy was 86.9%, while for Validation, the Accuracy was 57.4% (Fig. 1). Using the RestNet50 model, the precision was 90.506% during Training, while for Validation, it was 54.8% (Fig. 2). Finally, during the Testing stage, ResNet50 had a better performance, because EfficientNetB0 detected 80% for the negative class and 60% for the positive class. After the CNN models were trained, an additional validation stage was performed to compare the model results with the analysis made by a radiologist and a nephrologist. This comparison determined that the model performed better with images having VC, while it was less accurate with images having no presence of VC. This contrast could have different reasons, one being possible false-positive images. Conclusion This study created a CNN model using pre-trained models on top of modern architectures such as EfficientNet-B0 and ResNet50. Tools were used to prepare the images as well as to train, validate and test the model. The Transfer Learning helped to build a model faster, giving high Accuracy rates with only a few images due to the limited dataset. However, the model could give better results if a wider and balanced set of images is available. Also, a better pre-processing stage can be executed to reduce false-positive images. For future work, this model can be used as the core of a system that not only accurately detects Vascular Calcification from X-Ray images, but also can determine the level of calcification using measures such as the Kauppila Index or Adragao.

#2035 Use of ETElcalcetidefor pReserving vitamiN K-dependent proteInactivitYITAlian Study (ETERNITY-ITA Study)

Abstract Background and Aims Vascular calcifications (VCs) are prevalent in chronic kidney disease (CKD) and mineral disorders, associated with aortic calcification and increased bone fracture risk. The complex pathogenesis involves factors like calcium overload, phosphate imbalance, and secondary hyperparathyroidism. Key inhibitors, vitamin K-dependent proteins (VKDPs) like matrix Gla protein (MGP) and osteocalcin (BGP), play pivotal roles in VC development. Traditional VC risk reduction focuses on lowering parathyroid hormone, calcium and phosphorus levels. Etelcalcetide, a synthetic peptide activating the calcium-sensing receptor, demonstrates promise in clinical trials. Studies on Vitamin K Italian (VIKI) show that calcimimetic treated hemodialysis patients exhibit higher levels of total BGP and MGP, suggesting a protective effect. These findings underscore the multifactorial nature of VC in CKD, informing refined treatment strategies and targeted pathways for improved outcomes. The proposed study will investigate real world evidence of the effect of Etelcalcetide in preserving active forms of VKDPs among total MGP and total BGP with a resulting reduction of those inactive such as dephosphorylated-undercarboxylated MGP or dp-uc MGP, undercarboxylated BGP or uc-BGP, thus contributing to bone and vascular health in hemodialysis patients (Table 1). Method This is a multi-center comparative effectiveness observational longitudinal study with no predefined interference on drug dosing on the part of the investigators. The study will enroll 160 hemodialysis patients: 80 patients considered in the exposed group (treated with Etelcalcetide) and 80 age and sex matched patients considered in the unexposed group (treated with Calcitriol or vitamin D analogs). The treating nephrologist will base their target dose of Etelcalcetide on an individual-level patient basis in order to achieve the KDIGO PTH target level. In the Etelcalcetide-treated group, the addition of calcitriol is allowed when required by normal clinical practice (for correction of hypocalcemia) (Fig. 1). The primary endpoint is the comparison of the levels of active and non-active forms of VKDP between patients treated with Etelcalcetide and those treated with vitamin D or vitamin D analogues. The two groups will be compared for longitudinal changes in the following biomarkers, measured at baseline and after 3, 9, and 18 months of treatment: total MGP, dephosphorylated-undercarboxylated MGP, total BGP, and undercarboxylated BGP. As secondary outcomes will be evaluated bone vascular markers levels, serum calcification propensity T50 test, anemia markers levels, dialysis routine biomarkers, changes from baseline prevalence VCs (Aorta and Iliac arteries) and vertebral fractures by lateral Dorsal Lumbar spine x-Ray and Bone Mass Density (BMD) by Dual-energy X-ray absorptiometry (DEXA). Results Conclusion

Analysis of imaging characteristics and effectiveness of cervical spondylotic myelopathy with cervical kyphosis

To investigate the imaging characteristics of cervical kyphosis and spinal cord compression in cervical spondylotic myelopathy (CSM) with cervical kyphosis and the influence on effectiveness. The clinical data of 36 patients with single-segment CSM with cervical kyphosis who were admitted between January 2020 and December 2022 and met the selection criteria were retrospectively analyzed. The patients were divided into 3 groups according to the positional relationship between the kyphosis focal on cervical spine X-ray film and the spinal cord compression point on MRI: the same group (group A, 20 cases, both points were in the same position), the adjacent group (group B, 10 cases, both points were located adjacent to each other), and the separated group (group C, 6 cases, both points were located >1 vertebra away from each other). There was no significant difference between groups ( P>0.05) in baseline data such as gender, age, body mass index, lesion segment, disease duration, and preoperative C 2-7 angle, C 2-7 sagittal vertical axis (C 2-7 SVA), C 7 slope (C 7S), kyphotic Cobb angle, fusion segment height, and Japanese Orthopedic Association (JOA) score. The patients underwent single-segment anterior cervical discectomy with fusion (ACDF). The occurrence of postoperative complications was recorded; preoperatively and at last follow-up, the patients' neurological function was evaluated using the JOA score, and the sagittal parameters (C 2-7 angle, C 2-7 SVA, C 7S, kyphotic Cobb angle, and height of the fused segments) were measured on cervical spine X-ray films and MRI and the correction rate of the cervical kyphosis was calculated; the correlation between changes in cervical sagittal parameters before and after operation and the JOA score improvement rate was analyzed using Pearson correlation analysis. In 36 patients, only 1 case of dysphagia occurred in group A, and the dysphagia symptoms disappeared at 3 days after operation, and the remaining patients had no surgery-related complications during the hospitalization. All patients were followed up 12-42 months, with a mean of 20.1 months; the difference in follow-up time between the groups was not significant ( P>0.05). At last follow-up, all the imaging indicators and JOA scores of patients in the 3 groups were significantly improved when compared with preoperative ones ( P<0.05). The correction rate of cervical kyphosis in group A was significantly better than that in group C, and the improvement rate of JOA score was significantly better than that in groups B and C, all showing significant differences ( P<0.05), and there was no significant difference between the other groups ( P>0.05). The correlation analysis showed that the improvement rate of JOA score was negatively correlated with C 2-7 angle and kyphotic Cobb angle at last follow-up ( r=-0.424, P=0.010; r=-0.573, P<0.001), and positively correlated with the C 7S and correction rate of cervical kyphosis at last follow-up ( r=0.336, P=0.045; r=0.587, P<0.001), and no correlation with the remaining indicators ( P>0.05). There are three main positional relationships between the cervical kyphosis focal and the spinal cord compression point on imaging, and they have different impacts on the effectiveness and sagittal parameters after ACDF, and those with the same position cervical kyphosis focal and spinal cord compression point have the best improvement in effectiveness and sagittal parameters.

Active Rheumatoid Arthritis and Scoliosis: A Mid-Term Cohort Study.

Retrospective cohort study. To elucidate the factors related to progression of scoliosis in patients with rheumatoid arthritis (RA) using longitudinal cohort data. Thirty percent of patients with RA have lumbar scoliosis. However, the effectiveness of current treatment methods in preventing the progression of scoliosis is not well-understood due to a lack of longitudinal studies. We enrolled 180 patients with RA who were followed up for over two years, all of whom underwent standing spinal X-rays. These patients were categorized based on their disease activity score-28 with erythrocyte sedimentation rate (DAS28-ESR) into two groups: those in remission (n=76) and those in non-remission (n=104). We evaluated various radiographic measures, including C7 center sacral vertical line (C7-CSVL), pelvic obliquity, major Cobb angle, and curve location. Fifty-three (29.4%) patients presented progression of scoliosis during a mean follow-up period of 4.8 years. Patients in the non-remission showed larger Cobb angle at baseline and final follow-up, compared to those in remission. The annual progression rate of the curve was also greater in the non-remission group (1.04 degree /year), than in the remission group (0.59 degree /year, P=0.001). There was no difference in the incidence of new vertebral fractures. The presence of a baseline cobb angle of 10 degree or more (OR: 3.14; 95% CI: 1.38-7.13; P=0.006), glucocorticoid use (OR: 2.88; 95% CI: 1.18-7.06; P=0.021), and non-remission at baseline (OR: 2.83; 95% CI: 1.25-6.41; P=0.012) were significant risk factors for progression of scoliosis. RA disease activity is linked to progression of scoliosis in patients with RA. Patients with RA who present with an initial scoliosis of 10 degrees or greater, require glucocorticoids for treatment and are in non-remission at baseline may be at high risk for scoliosis progression.

Tuina Inhibits Chondrocyte Apoptosis by Regulating the FAK/PI3K/Akt Pathway in Rabbits with IVDD.

Intervertebral disc degeneration (IVDD), a key contributor to degenerative spinal diseases such as cervical spondylosis, significantly influences the quality of life of patients. Tuina, historically employed in the clinical management of cervical spondylosis, has demonstrated positive therapeutic outcomes; however, the mechanism of Tuina remains unclear. This study examined the efficacy of Tuina in correcting the imbalanced structure of the cervical spine and its impact on apoptotic chondrocytes within the cervical disc. The underlying mechanisms were explored using a rabbit model of IVDD induced by dynamic and static imbalances. The IVDD rabbit model was established by restraining the head in a downward position for 12 weeks (Model group). In the Tuina1 group, treatment was performed on the posterior cervical trapezius muscle daily for 2 weeks, whereas in the Tuina2 group, treatment was performed on both the posterior cervical trapezius and anterior sternocleidomastoid muscles daily for 2 weeks. After treatment, X-ray, micro-computed tomography (CT), histological staining, qRT-PCR, and western blotting were used to evaluate the mechanism by which Tuina inhibits chondrocyte apoptosis. The results demonstrated that Tuina treatment inhibited chondrocyte apoptosis in cervical discs by adjusting the neck structure balance, and a more significant therapeutic effect was observed in the Tuina2 group. Lateral cervical spine X-ray and CT scans in rabbits revealed notable improvements in cervical spine curvature and vertebral structure in the treatment groups compared with those in the Model group. Hematoxylin and eosin staining and TUNEL staining further confirmed the positive impact of Tuina treatment on intervertebral disc tissue morphology and chondrocyte apoptosis. Additionally, western blotting and immunohistochemical analysis showed that Tuina treatment suppressed chondrocyte apoptosis by downregulating Bax and caspase-3 while upregulating Bcl-2. Western blotting results further indicated that Tuina could activate the FAK/PI3K/Akt signaling pathway by mediating integrin-β1. Tuina treatment inhibited chondrocyte apoptosis in cervical discs by activating the FAK/PI3K/Akt signaling pathway, providing convincing evidence to support Tuina treatment as a promising method for IVDD.

Abstract PO1-12-10: Zoledronic acid to prevent rebound fractures after stopping adjuvant denosumab: a randomized controlled sub-study of the ABCSG-18 trial

Abstract Introduction ABCSG-18 trial showed that 60mg denosumab subcutaneously every 6 months halves fracture risk in postmenopausal patients with early breast cancer, who are treated with adjuvant aromatase inhibitors. However, it has been reported that stopping denosumab may lead to overactivation of osteoclasts resulting in so called rebound vertebral fractures. As shown previously, zoledronic acid (ZA), when given to patients of the ABCSG-18 trial after stopping denosumab prevents a high bone turnover state that can occur in some patients. Here we report the impact of a single ZA dose on bone mineral density (BMD) and fractures after stopping denosumab. Methods ABCSG-18 trial patients, who had received adjuvant denosumab were re-randomized to receive a single dose of ZA 5mg i.v. or not, 8 months +- 4 weeks after their last denosumab dose. DXA scans, lateral spine X-rays and bone turnover markers (CTX and osteocalcin) were assessed at baseline and at 6, 12 and 18 months thereafter. New clinical fractures were assessed until 18 months after randomization. Results 50 patients were randomized to either ZA (24pts) or control (26pts). All patients of the ZA arm and 25 out of 26 patients of the control arm had received 7 doses of denosumab within the ABCSG 18 trial before re-randomization. Baseline bone health factors were well balanced between the 2 arms: Baseline BMD did not differ between the two arms with regard to femoral neck (ZA: 0.817 g/cm2; SOC: 0.816g/cm2), total hip (ZA: 0.918g/cm2; SOC: 0.921g/cm2) and lumbar spine (ZA: 1.128 g/cm2; SOC 1.142 g/cm2), respectively. Relative changes in BMD differed significantly between both arms for femoral neck after 12 months (ZA: 0.5%; control: -4.98%, p 0.004) and for total hip after 6 months (ZA: -0.73%; control: -4.61%, p&amp;lt; 0.001) and after 12 months (ZA: -0.13%; control: -5.62%; p&amp;lt; 0.001). There was a trend towards a greater decline in BMD at lumbar spine after 6 months in the control arm versus the ZA arm (-3.35% vs 0.67%, p=0.052). After randomization, 3 asymptomatic fractures (lumbar vertebral and thoracic vertebral in one patient and thoracic vertebral in a second patient) occurred in the ZA arm, whereas 2 clinical fractures (one radius fracture and one lumbar vertebral fracture) occurred in the SOC arm. Conclusion A single dose of Zoledronic acid prevents high bone turnover state resulting in loss of BMD in some patients after stopping denosumab. ZA may therefore be offered to breast cancer patients with aromatase inhibitor after stopping denosumab. Citation Format: Georg Pfeiler, Christian F. Singer, Dominik Hlauschek, Manfreda Diether, Ferdinand Haslbauer, Paul Sevelda, Kristin Köck, Karl Tamussino, Arno C. Reichenauer, Florian Fitzal, Dietmar Heck, Richard Greil, Claudia Pasterk, Sonja Heibl, Walter Herz, Marija Balic, Daniel Egle, Lidija Sölkner, Michael Gnant. Zoledronic acid to prevent rebound fractures after stopping adjuvant denosumab: a randomized controlled sub-study of the ABCSG-18 trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-12-10.

Prevalence of Transitional Vertebra in Lumbar Disc Herniation in Bangabandhu Sheikh Mujib Medical University

Background: Lumbosacral transitional vertebrae (LSTV) occur as a congenital anomaly in the segmentation of lumbosacral spine that occur during intrauterine life. LSTV includes lumbarization and sacralization of the lumbosacral region. A transitional vertebra(TV) may have varying formations, the common feature being an atypical lumbosacral articulation between transverse process of the most inferior lumbar vertebra and the sacrum. There has been a lot of discussion regarding the prevalence of LSTV in lumbar disc herniation(LDH). Most of the studies showed increased prevalence while other studies showed TV is an incidental finding and there is no increased prevalence in LDH. LDH is a quiet common ailment encountered in neurosurgical practice. Numerous causes have been attributed to it. LSTV might have great importance in patients who clinically seem to have LDH where it’s presence in plain X-ray might provide a supportive evidence for diagnosis and it can help to counsel the patient. We will be able to recommend a plain x-ray of lumbosacral spine first, having history and clinical findings suggestive of LDH and can save the cost of MRI investigation until decision for operation. Beside this LSTV is an important entity for spinal surgeons , radiologists and also for those who do interventional procedures in the spine. For this reason the prevalence of TV in LDH should be known. This study was not carried out in our country yet. So this study will enrich our demographic information and will also help the spinal surgeons to counsel the patients about their congenital spinal morphological variation and different facts related with this. Objectives: General objective of this study is to observe the prevalence of LSTV in patients with LDH. Specific objective of this study was to identify the diathroidal joint or fusion between transverse process of last lumbar vertebra and ala of sacrum, to count the vertebral number from C2 to S1 in whole spine screening film, to identify the transitional vertebra as sacralization or lumbarization or absence of TV, to predict the future possibility of development of LDH from plain X-ray of lumbosacral spine. Methods: This study was a cross sectional type of observational study and was conducted in Department of Neurosurgery, Bangabandhu Sheikh Mujib Medical University (BSMMU). The patients (N=45) who fulfilled the selection criteria was enrolled in this study. Patient’s data were recorded in a predetermined data sheet. Patients were informed in details about the study, its merits and demerits in easy and understandable language and then informed consent was taken. Also assurance was given that all the information and records would be kept confidential and the study result would help the neurosurgeons to counsel the patients with LSTV about future possibility of development of LDH from plain X-ray of lumbosacral spine. This study was not responsible for any additional harm to the patient and study had no potential risk to the patient and no experimental drugs were used in this study. Results: A total of 45 patients of LDH were studied to see the prevalence of lumbosacral TV in LDH. Based on history and clinical findings, 100% of patients presented with low back pain, 100% of patients presented with sciatica, and 62.2% presented with gait difficulty and 02.2% of patients presented with cauda equina syndrome. Mean age of patients having LDH herniation was 38.08 ± 10.15 years. LSTV was found in 31.11% of patients with LDH. In this study, LSTV was diagnosed by plain X ray of Lumbosacral spine (A/P view) and supplemented with MRI of L/S spine with screening of whole spine (in most cases). Diagnosis of transitional vertebra was done by researcher and was further confirmed by guide and faculty members of BSMMU. In relation to gender, prevalence of LSTV in male was 25.9% and in female was 38.9% which indicated higher prevalence of TV in female but this is not statistically significant (p value 0.356).Incidence of disc herniation in patients with TV was at the space above the transition in 85.7% of cases. The most likely explanation for this is that the motion segment cephalad to the LSTV has to bear additional stresses by virtue of it being juxtaposed to a relatively non-mobile segment Conclusion: This study reveals that the prevalence of LSTV in LDH is 31.11% which is near the upper limit of its prevalence(according to literature). Association of TV with LDH can not be obtained as a control group of patient without LDH was not considered. So further study should be carried out incorporating large number of patients with control group with long study period to generalize the findings to target population. Bang. J Neurosurgery 2023; 12(2): 76-85

Tuberculous Spondylodiscitis or Pott's Disease: Diagnostic Problem of Patients Referred to the Reference Health Center (CSRef) Commune III of Bamako for Anti-Tuberculosis Treatment

Introduction: Tuberculosis is an infectious disease that can affect different organs. The spinal disorder is called tuberculous spondylodiscitis (Pott's disease). The objective: Was to elucidate the aspects taken into account by caregivers in the diagnosis of tuberculosis spondylodiscitis in patients referred to the reference health center of commune III of Bamako for anti-tuberculosis treatment. Methodology: This was a descriptive, retrospective study covering all patients referred to CSRef CIII from different health structures with the diagnosis of Pott's disease for treatment during the study period. Results: During the study period, we collected 156 patients. The male sex represented 54% with a sex ratio of 1.18. The average age was 39.3 years with extremes of 12 and 92 years. All patients presented with spinal pain, fever was present in 11.11% of patients and 2.7% had respiratory symptoms. Tuberculin IDR was positive in 95.8% and 2 patients were AFB sputum positive. X-ray (of the lumbar spine) was negative in 18%; there were images of degenerative lesions in 41.6% and only 40.2% of patients had presumptive radiological signs. CT was carried out in all patients revealing 33.33% of sequelae of fractures and degenerative lesions and 55.55% of presumptive signs of tuberculous spondylodiscitis. Only 41.66 of the cases were referred by specialist doctors. Conclusion: In our context, the diagnosis of Pott's disease seems to be based mainly on chronic spinal pain and positive IDR and is not often made by appropriate personnel.

Relationship Between Serum 25-Hydroxyvitamin D and Bone Mineral Density, Fracture Risk, and Bone Metabolism in Adults With Osteoporosis/Fractures

ObjectiveTo evaluate the association of serum 25-hydroxyvitamin D (25(OH) D) levels with bone mineral density (BMD), fracture risk, and bone metabolism. MethodsThis multicenter cross-sectional study recruited menopausal females and males greater than or equal to 50 year old with osteoporosis/fractures between September 2016 and September 2021. Assessment included clinical data, 25(OH)D, intact parathyroid hormone (iPTH), procollagen type 1 amino-terminal propeptide (P1NP), carboxy-terminal collagen crosslinks (CTX), lateral thoracolumbar spine x-rays, and BMD. ResultsA total of 3003 individuals were stratified by 25(OH) D levels: 720 individuals (24%) <20 ng/mL, 1338 individuals (44.5%) 20 to 29 ng/mL, and 945 individuals (31.5%) ≥30 ng/mL. In unadjusted and multivariable models, BMD T-score, except spine, was significantly and positively associated with 25(OH)D levels. 25(OH) D levels were inversely associated with Fracture Risk Assessment Tool scores. Patients with 25(OH)D <20 ng/mL had significantly higher iPTH and bone turnover markers (P1NP and CTX) than patients with 25(OH)D ≧20 ng/mL in all models. When analyzing bone-related markers and BMD, total hip and femoral neck BMD T-scores were positively correlated with 25(OH)D concentrations and BMI but negatively correlated with iPTH, P1NP, CTX, and age. In multivariate models with all bone-related markers, only 25(OH)D levels were significantly associated with total hip and femoral neck BMD. ConclusionVitamin D deficiency is significantly associated with decreased total hip and femoral neck BMD and increased fracture risk as assessed by Fracture Risk Assessment Tool. In those with osteoporosis/fractures, vitamin D is implicated in the causal relationship between bone remodeling and BMD. Assessing vitamin D status is imperative for those at risk for osteoporosis/fractures.

Epidemiological, clinical and paraclinical aspects of Pott's disease at Brazzaville University Hospital

Objective: Describe the epidemiological, clinical and paraclinical aspects of Pott's disease at the University Hospital Center of Brazzaville. Patients and methods: This was a descriptive, cross-sectional study with retrospective collection carried out in the Rheumatology department of Brazzaville University Hospital from January 1, 2013 to June 31, 2017, i.e. 4 years and 6 months. The diagnosis of Pott's disease was based on anamnestic, clinical and paraclinical arguments. Results: The hospital frequency of Pott's disease was 6.4%. Men predominated (56.4%) with a sex ratio of 1.3. The average age was 46 +/- 16 years (range: 20-80 years). The average time to diagnosis was 6.6 ± 5 months (range: 1-26 months). Alcoholism was the most common cause in 56 cases (50.9%). Spinal syndrome was present in 109 cases (99.1%). All patients had a standard spine x-ray performed. Computed tomography was performed in 28 patients (25.5%), magnetic resonance imaging in 23 patients (20.9%). The most common morphological aspect was spondylodiscitis in 76 cases (69.1%) associating disc pinching (60%), erosion (40%), and demineralization (19.1%). Atypical forms were found in 34 cases (30.9%), dominated by the appearance of spondylitis in 21 cases (61.8%). Conclusion: Pott's disease is a common pathology in developing countries, spondylodiscitis remains the most common morphological form.

Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images

The spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.