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Manual method for measuring the Cobb-angle (available from: https://www. spinemd.com/news-philanthropy/scoliosismeasurements).
Source publication
Background: Scoliosis is the most common type of spinal deformity. A universal
and standard method for evaluating scoliosis is Cobb angle measurement, but several
studies have shown that there is intra- and inter- observer variation in measuring
cobb angle manually.
Objective: Develop a computer- assisted system to decrease operator-dependent
error...
Context in source publication
Citations
... The ages of the participants reflected the purpose of the outcome measure used. Two studies [81,82] utilizing Cobb angles did consider participants as older than 18 years. The sex of the participants was distributed evenly except for postural assessment where males were exclusively studied. ...
... As a result, the thinner borders could vanish in X-ray, making identification increasingly hard. As a result, pre-processing is required in order to decrease picture noise [24]. This research contributes to an image processing approach that is applicable to image pixels and handles luminosity. ...
Scoliosis seems to be the most frequently used type of spinal abnormality. Cobb angle measurement essentially relates to the quantification of the spinal stenosis in degrees, also it is a globally standardized approach for assessing scoliosis. Based on a recent survey, there really is no accurate and comprehensive technique for calculating the Cobb angle programmatically. This problem is crucial in the medical field pertaining to Cobb angle measurement to identify the exact position of Cobb in X-ray images. However, multiple investigations have demonstrated that there is inter and intra-observer variance when assessing the Cobb angle physically. The goal is to create a computer-assisted solution to reduce user-based Cobb angle measuring errors. The preprocessing filters and semi-automatic methods determine the overall architectural curved spine. Using the Cobb technique, results are inaccuracies and improper in the estimation of a scoliotic curvature's peak or bottom vertebra. The curve-fitting approach was used in this investigation to reduce uncertainty. Every patient had a digitally recorded poster anterior radiography image. The polynomial estimation is fitted using user-defined curvature midpoints that correspond to vertebral intersection points. The Cobb angle is computed by taking the first characteristic of the fitted polynomial function and dividing it by the number of vertebrae.
... Our method has been proven to accurately detect vertebral landmarks under various X-ray image conditions. Previous deep learning methods had limited detection of vertebrae at high-angle curves (above 40°), resulting in lowered CA calculation accuracy [15], [35]. In high curve deformity (above 60°), Auto-CA still could accurately detect each vertebra. ...
... The previous method uses AI with a semi-automatic algorithm for CA calculations [35]. Users were required to select several vertebrae boundaries manually. ...
An accurate identification and localization of vertebrae in X-ray images can assist doctors in measuring Cobb angles for treating patients with adolescent idiopathic scoliosis. It is useful for clinical decision support systems for diagnosis, surgery planning, and spinal health analysis. Currently, publicly available annotated datasets on spinal vertebrae are small, making deep-learning-based detection methods that are highly data-dependent less accurate. In this paper, we propose an algorithm based on convolutional neural networks that can be trained to detect vertebrae from a small set of images. This method can display critical information on a patient's spine, display vertebrae and their labels on the thoracic and lumbar, calculate the Cobb angle, and evaluate the severity of spinal deformities. The proposed achieved an average accuracy of 0.958 and 0.962 for classifying spinal deformities (i.e., C-shaped, S-shaped type 1, and S-shaped type 2) and severity of Cobb angle (i.e., normal, mild, moderate, and severe), respectively. The Cobb angle measurement had a median difference of less than 5° from the ground-truth with SMAPE of 5.27% and an error on landmark detection of 19.73. In addition, Lenke classification is used to analyze spinal deformities as types A, B, and C, which have an average accuracy of 0.924. Physicians can use the proposed system in clinical practice by providing X-ray images via the user interface.
... The pathophysiological effect of scoliosis is complex and multiorgan. Besides musculoskeletal disturbances, cardiovascular, pulmonary, or psychosocial effects are described [1][2][3][4]. Scoliosis is categorized according to the etiology. ...
Scoliosis is the most frequent spinal deformity in children. It is defined as a spine deviation of more than 10° in the frontal plane. Neuromuscular scoliosis is associated with a heterogeneous spectrum of muscular or neurological symptoms. Anesthesia and surgery for neuromuscular scoliosis have a higher risk of perioperative complications than for idiopathic scoliosis. However, patients and their relatives report improved quality of life after the surgery. The challenges for the anesthetic team result from the specifics of the anesthesia, the scoliosis surgery itself, or factors associated with neuromuscular disorders. This article includes details of preanesthetic evaluation, intraoperative management, and postoperative care in the intensive care unit from an anesthetic view. In summary, adequate care for patients who have neuromuscular scoliosis requires interdisciplinary cooperation. This comprehensive review covers information about the perioperative management of neuromuscular scoliosis for all healthcare providers who take care of these patients during the perioperative period, with an emphasis on anesthesia management.
... Abnormal alignment of the spine itself is a diagnostic criterion for several spinal diseases, such as scoliosis and spondylolisthesis [1,2], and it is also a major factor of low back pain and spinal instability [3,4]. These pathological conditions of the spine are usually diagnosed through radiography (especially X-ray images) because it can quickly acquire the pathological condition and location information of the bone, has low radiation exposure, and has wide availability [5,6]. Recently, several methods for diagnosing spinal misalignment based on medical images, such as ultrasound, CT, and MRI, have been developed, but these are extensions of X-ray-based measurement, which is still considered the gold standard [7][8][9][10]. ...
Vertebral landmark labelling on X-ray images is important for objective and quantitative diagnosis. Most studies related to the reliability of labelling focus on the Cobb angle, and it is difficult to find studies describing landmark point locations. Since points are the most fundamental geometric feature that can generate lines and angles, the assessment of landmark point locations is essential. The aim of this study is to provide a reliability analysis of landmark points and vertebral endplate lines with a large number of lumbar spine X-ray images. A total of 1000 pairs of anteroposterior and lateral view lumbar spine images were prepared, and 12 manual medicine experts participated in the labelling process as raters. A standard operating procedure (SOP) was proposed by consensus of the raters based on manual medicine and provided guidelines for reducing sources of error in landmark labelling. High intraclass correlation coefficients ranging from 0.934 to 0.991 verified the reliability of the labelling process using the proposed SOP. We also presented means and standard deviations of measurement errors, which could be a valuable reference for evaluating both automated landmark detection algorithms and manual labelling by experts.
... Cobb angle calculation on the scoliosis[21] 3. Pelvis line (Pe): The 2 points selected for the posterior view is PSISs and ASISs for the anterior view. The line is then connected between the two points. ...
Spine curvature disorders are scoliosis, lordosis, and kyphosis. These disorders are mainly caused by the bad habits of the person during sitting, standing, and lying. There are about 3 to 5 out of 1,000 people who are affected by spine curvature disorder. The current conventional method used for diagnose this disorder, such as radiography, goniometry and palpation. However, these conventional methods require human skills and can be time-consuming, resulting to exhaustion of logistic. Therefore, there is a need to solve this problem by creating a Graphical User Interface (GUI) to analyse the human body posture through the 3D reconstructed model of the person. Hence, 3D map meshing reconstruction of the human body method is proposed. This project divided into three parts, which are the development of the GUI for human posture analysis, clinical validation and posture analysis of the 3D model. The 3D model reconstructed from 3D mapping parameters shows 100% accuracy of the assessed point. The lowest difference of angle for the comparison between clinical method (goniometer) and the GUI for male is (A.Pe) 0.930±0.870 and 1.240±0.860 for female (P.Pe). This finding of 3D model assessment system can be helpful for medical doctor to diagnose patient who have spine problem.
... To guide the change of the brace shape, we need a merit function that quantifies the impact on the spine from a clinical standpoint. In our approach, we use the Cobb angle (Safari et al., 2019) to characterize the severity of scoliosis. More complex, multi-objective merit functions are left for future work, but are briefly discussed in Section 6. ...
... As discussed in the definition of the clinical metric in Section 4.1, in our study we use the Cobb angle metric Frontiers in Bioengineering and Biotechnology frontiersin.org 07 (Safari et al., 2019) to characterize the severity of scoliosis, as it is the standard clinical metric for this purpose. The average Cobb angle measured on the subjects was 17.4°(±8.9°). ...
This work describes a computational methodology for the design of braces for adolescent idiopathic scoliosis. The proposed methodology relies on a personalized simulation model of the patient’s trunk, and automatically searches for the brace geometry that optimizes the trade-off between clinical improvement and patient comfort. To do this, we introduce a formulation of differentiable biomechanics of the patient’s trunk, the brace, and their interaction. We design a simulation model that is differentiable with respect to both the deformation state and the brace design parameters, and we show how this differentiable model is used for the efficient update of brace design parameters within a numerical optimization algorithm. To evaluate the proposed methodology, we have obtained trunk models with personalized geometry for five patients of adolescent idiopathic scoliosis, and we have designed Boston-type braces. In a simulation setting, the designed braces improve clinical metrics by 45% on average, under acceptable comfort conditions. In the future, the methodology can be extended beyond synthetic validation, and tested with physical braces on the actual patients.
... Scoliosis is a health condition. Cobb angle can be measured accurately and quickly by applying the principle that the cobb angle is equal to the sum of the tilt angles of the upper and lower end vertebrae, ensuring that scanning film data is not easily contaminated, [20]. The average measuring error under special conditions is 3°. ...
Chronic back pain is a bending-induced malformation of the human spinal column that can cause severe pain as well as cosmetic and pulmonary issues. The external appearance of a human back in scoliosis is generally the reflection of internal deformation. Spinal curvature is usually measured in degrees using the Cobb angle, the standard method for evaluating scoliosis patients. This article highlights the review of earlier research articles on scoliosis to provide insight into the existing knowledge, which aids in the robust identification and monitoring of scoliosis. However, many researchers have worked in this field for many decades yet there is no reliable, easily available, and universal tool for Cobb angle estimation. Hence, the present article enlightens the existing information and the lacunae in the field to aid further scope for research opportunities available for future consideration. Using RGB and complexity photos collected by an RGB-complexity device Microsoft, a modified convolutional network (MCN) named fuse-Unet is the proposal to provide automatic recognition of the human spine area and which was before the imaging route. A normal-vector-based approach and two force sensors are used to ensure that the probe fits the spine area well a 6-degree-of-freedom robotic arm in the role of a doctor who completes the automatic scanning along the pre-planned path. Furthermore, Cobb angles for morphological structural analysis of the spine are determined using 3-D ultrasound modeling and scanning of the spine. The suggested system's performance is evaluated using phantom and in vivo tests.
... Therefore, it is difficult for inexperienced observers to measure Cobb angles accurately. Besides, inevitable manual operations increase the work burden [3]. ...
Purpose
The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology.
Methods
A total of 181 anterior–posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts.
Results
The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00–91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00–88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 ( p < 0.001).
Conclusion
The automated measurement results agreed with the experts’ annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.
... In [37], Safari et al. developed a semi-manual approach for the estimation of Cobb angle. Contract stretching is used to extract the ROI in an input X-ray image. ...
The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29 and 0.38, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.
