Computer-aided assessment of scoliosis on posteroanterior radiographs

Automatic Vertebral Rotation Angle Measurement of 3D Vertebrae Based on an Improved Transformer Network

The measurement of vertebral rotation angles serves as a crucial parameter in spinal assessments, particularly in understanding conditions such as idiopathic scoliosis. Historically, these angles were calculated from 2D CT images. However, such 2D techniques fail to comprehensively capture the intricate three-dimensional deformities inherent in spinal curvatures. To overcome the limitations of manual measurements and 2D imaging, we introduce an entirely automated approach for quantifying vertebral rotation angles using a three-dimensional vertebral model. Our method involves refining a point cloud segmentation network based on a transformer architecture. This enhanced network segments the three-dimensional vertebral point cloud, allowing for accurate measurement of vertebral rotation angles. In contrast to conventional network methodologies, our approach exhibits notable improvements in segmenting vertebral datasets. To validate our approach, we compare our automated measurements with angles derived from prevalent manual labeling techniques. The analysis, conducted through Bland-Altman plots and the corresponding intraclass correlation coefficient results, indicates significant agreement between our automated measurement method and manual measurements. The observed high intraclass correlation coefficients (ranging from 0.980 to 0.993) further underscore the reliability of our automated measurement process. Consequently, our proposed method demonstrates substantial potential for clinical applications, showcasing its capacity to provide accurate and efficient vertebral rotation angle measurements.

A Dual Coordinate System Vertebra Landmark Detection Network with Sparse-to-Dense Vertebral Line Interpolation

Precise surveillance and assessment of spinal disorders are important for improving health care and patient survival rates. The assessment of spinal disorders, such as scoliosis assessment, depends heavily on precise vertebra landmark localization. However, existing methods usually search for only a handful of keypoints in a high-resolution image. In this paper, we propose the S2D-VLI VLDet network, a unified end-to-end vertebra landmark detection network for the assessment of scoliosis. The proposed network considers the spatially relevant information both from inside and between vertebrae. The new vertebral line interpolation method converts the training labels from sparse to dense, which can improve the network learning process and method performance. In addition, through the combined use of the Cartesian and polar coordinate systems in our method, the symmetric mean absolute percentage error (SMAPE) in scoliosis assessment can be reduced substantially. Specifically, as shown in the experiments, the SMAPE value decreases from 9.82 to 8.28. The experimental results indicate that our proposed approach is beneficial for estimating the Cobb angle and identifying landmarks in X-ray scans with low contrast.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

Validity and Absolute Reliability of the Cobb Angle in Idiopathic Scoliosis with TraumaMeter Software

The Cobb angle value is a critical parameter for evaluating adolescent idiopathic scoliosis (AIS) patients. This study aimed to evaluate a software’s validity and absolute reliability to determine the Cobb angle in AIS digital X-rays, with two different degrees of experienced observers. Four experts and four novice evaluators measured 35 scoliotic curves with the software on three separate occasions, one month apart. The observers re-measured the same radiographic studies on three separate occasions three months later but on conventional X-ray films. The differences between the mean bias errors (MBE) within the experience groups were statistically significant between the experts (software) and novices (manual) (p < 0.001) and between the novices (software) and novices (manual) (p = 0.005). When measured with the software, the intra-group error in the expert group was MBE = 1.71 ± 0.61° and the intraclass correlation coefficient (ICC (2,1)) = 0.986, and in the novice group, MBE = 1.9 ± 0.67° and ICC (2,1) = 0.97. There was almost a perfect concordance among the two measurement methods, ICC (2,1) = 0.998 and minimum detectable change (MCD95) < 0.4°. Control of the intrinsic error sources enabled obtaining inter- and intra-observer MDC95 < 0.5° in the two experience groups and with the two measurement methods. The computer-aided software TraumaMeter increases the validity and reliability of Cobb angle measurements concerning manual measurement.

Validity and reliability of a computer-assisted system method to measure axial vertebral rotation

BACKGROUND

Axial vertebral rotation and Cobb's angle are essential parameters for analysing adolescent idiopathic scoliosis. This study's scope evaluates the validity and absolute reliability of application software based on a new mathematical equation to determine the axial vertebral rotation in digital X-rays according to Raimondi's method in evaluators with different degrees of experience.

METHODS

Twelve independent evaluators with different experience levels measured 33 scoliotic curves in 21 X-rays with the software on three separate occasions, separated one month. Using the same methodology, the observers re-measured the same radiographic studies three months later but on X-ray films and in a conventional way.

RESULTS

Both methods show good validity and reliability, and the intraclass correlation coefficients are almost perfect. According to our results, the software increases 1.7 times the validity and 1.9 times the absolute reliability of axial vertebral rotation on digital X-rays according to Raimondi's method, compared to the conventional manual measurement.

CONCLUSIONS

The intra-group and inter-group agreement of the measurements with the software shows equal or minor variations than with the manual method, among the different measurement sessions and in the three experience groups. There is almost perfect agreement between the two measurement methods, so the equation and the software may be helpful to increase the accuracy in the axial vertebral rotation assessment.

MPF-net: An effective framework for automated cobb angle estimation

In clinical practice, the Cobb angle is the gold standard for idiopathic scoliosis assessment, which can provide an important reference for clinicians to make surgical plan and give medical care to patients. Currently, the Cobb angle is measured manually on both anterior-posterior(AP) view X-rays and lateral(LAT) view X-rays. The clinicians first find four landmarks on each vertebra, and then they extend the line from landmarks and measure the Cobb angle by rules. The whole process is time-consuming and subjective, so that the automated Cobb angle estimation is required for efficient and reliable Cobb angle measurement. The noise in X-rays and the occlusion of vertebras are the main difficulties for automated Cobb angle estimation, and it is challenging to utilize the information between the multi-view X-rays of the same patient. Addressing these problems, in this paper, we propose an effective framework named MPF-net by using deep learning methods for automated Cobb angle estimation. We combine a vertebra detection branch and a landmark prediction branch based on the backbone convolutional neural network, which can provide the bounded area for landmark prediction. Then we propose a proposal correlation module to utilize the information between neighbor vertebras, so that we can find the vertebras hidden by ribcage and arms on LAT X-rays. We also design a feature fusion module to utilize the information in both AP and LAT X-rays for better performance. The experiment results on 2738 pair of X-rays show that our proposed MPF-net achieves precise vertebra detection and landmark prediction performance, and we get impressive 3.52 and 4.05 circular mean absolute errors on AP and LAT X-rays respectively, which is much better than previous methods. Therefore, we can provide clinicians with automated, efficient and reliable Cobb angle measurement.

A Survey of Methods and Technologies Used for Diagnosis of Scoliosis

The purpose of this article is to present diagnostic methods used in the diagnosis of scoliosis in the form of a brief review. This article aims to point out the advantages of select methods. This article focuses on general issues without elaborating on problems strictly related to physiotherapy and treatment methods, which may be the subject of further discussions. By outlining and categorizing each method, we summarize relevant publications that may not only help introduce other researchers to the field but also be a valuable source for studying existing methods, developing new ones or choosing evaluation strategies.

Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Analysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

Developing of a Mathematical Model to Perform Measurements of Axial Vertebral Rotation on Computer-Aided and Automated Diagnosis Systems, Using Raimondi's Method

Introduction

Axial vertebral rotation (AVR) is a basic parameter in the study of idiopathic scoliosis and on physical two-dimensional images. Raimondi's tables are the most used method in the quantification of AVR. The development of computing technologies has enabled the creation of computer-aided or automated diagnosis systems (CADx) with which measurement on medical images can be carried out more quickly, simply, and with less intra and interobserver variabilities than manual methods. Although there are several publications dealing with the measurement of AVR in CADx systems, none of them provides information on the equation or algorithm used for the measurement applying Raimondi's method. Goal. The aim of this work is to perform a mathematical modelling of the data contained in Raimondi's tables that enable the Raimondi method to be used in digital medical images more precisely and in a more exact manner.

Methods

Data from Raimondi's tables were tabulated on a first step. After this, each column of Raimondi's tables containing values corresponding to vertebral body width (D) were adjusted to a curve determined by AVR = f (d). Third, representative values of each rotation divided by D were obtained through the equation of each column D. In a fourth step, a regression line was fitted to the data in each row, and from its equation, the mean value of the D/d distribution is calculated (value corresponding to the central column, D = 45). Finally, a curve was adjusted to the obtained data using the least squares method. Summary and Conclusion. Our mathematical equation allows the Raimondi method to be used in digital images of any format in a more accurate and simplified approach. This equation can be easily and freely implemented in any CADx system to quantify AVR, providing a more precise use of Raimondi's method, as well as being used in traditional manual measurement as it is performed with Raimondi tables.

Mobile Applications for Assessing Human Posture: A Systematic Literature Review

Smartphones are increasingly incorporated with features such as sensors and high resolution cameras that empower their capabilities, enabling their use for varied activities including human posture assessments. Previous reviews have discussed methods used in postural assessment but none of them focused exclusively on mobile applications. This paper systematically reviews mobile applications proposed for analyzing human posture based on alignment of the body in the sagittal and coronal plane. The main digital libraries were searched, 26 articles published between 2010 and 2020 were selected, and 13 mobile applications were identified, classified and discussed. Results showed that the use of mobile applications to assist with posture assessment have been demonstrated to be reliable, and this can contribute to clinical practice of health professionals, especially the assessment and reassessment phases of treatments, despite some variations when compared to traditional methods. Moreover, in the case of image-based applications, we highlight the advantage that measurements can be taken with the assessor at a certain distance with respect to the patient’s position, which is an important function for assessments performed in pandemic times such as the outbreak of COVID-19.

Newfangled Procedures Using X-ray to Determine the Cobb Angle in Patients with Scoliosis: An Updated Systematic Review

BACKGROUND

Scoliosis is a three-dimensional deformity of the spine. It is usually assessed by measuring Cobb angle. Nowadays, due to increasing effectiveness of image processing and machine vision, willingness to apply these methods has improved considerably in determining scoliosis of Cobb angle.

METHODS

In accordance with the PRISMA guideline, a broad electronic search of all English language literature was conducted on the topic through four databases, including MEDLINE, Web of Science, Scopus, and the Cochrane Library from 2012 (last search date from earlier review) to 30 March 2017.

RESULTS

Twelve studies were included. 90% labeled high-quality were selected for analysis. Eighty percent of the selected studies were published in the period between 2012 and 2017. Three new procedures were used to measure the Cobb angle. One study used automated procedure (7%), two studies used smartphone procedure (14%), and nine studies used the semiautomatic procedure of Cobb measurement (79%). Seven studies reported reproducibility and repeatability. Reproducibility range was 0.72 to 1 in reporting of ICC. Repeatability has a high range in three separated methods.

CONCLUSION

Computerized assessment method (Automatic and Semi-automatic) is most commonly performed in Cobb measurement. Semi-automatic is an effective measurement option for computerized assessment Cobb angle. There is no significant difference between manual, computer- based, and smartphone-based methods in described measures.

An Application of Artificial Intelligence to Diagnostic Imaging of Spine Disease: Estimating Spinal Alignment From Moiré Images

The use of artificial intelligence (AI) as a tool supporting the diagnosis and treatment of spinal diseases is eagerly anticipated. In the field of diagnostic imaging, the possible application of AI includes diagnostic support for diseases requiring highly specialized expertise, such as trauma in children, scoliosis, symptomatic diseases, and spinal cord tumors. Moiré topography, which describes the 3-dimensional surface of the trunk with band patterns, has been used to screen students for scoliosis, but the interpretation of the band patterns can be ambiguous. Thus, we created a scoliosis screening system that estimates spinal alignment, the Cobb angle, and vertebral rotation from moiré images. In our system, a convolutional neural network (CNN) estimates the positions of 12 thoracic and 5 lumbar vertebrae, 17 spinous processes, and the vertebral rotation angle of each vertebra. We used this information to estimate the Cobb angle. The mean absolute error (MAE) of the estimated vertebral positions was 3.6 pixels (~5.4 mm) per person. T1 and L5 had smaller MAEs than the other levels. The MAE per person between the Cobb angle measured by doctors and the estimated Cobb angle was 3.42°. The MAE was 4.38° in normal spines, 3.13° in spines with a slight deformity, and 2.74° in spines with a mild to severe deformity. The MAE of the angle of vertebral rotation was 2.9°±1.4°, and was smaller when the deformity was milder. The proposed method of estimating the Cobb angle and AVR from moiré images using a CNN is expected to enhance the accuracy of scoliosis screening.

Vertebral rotation estimation from frontal X-rays using a quasi-automated pedicle detection method

PURPOSE

Measurement of vertebral axial rotation (VAR) is relevant for the assessment of scoliosis. Stokes method allows estimating VAR in frontal X-rays from the relative position of the pedicles and the vertebral body. This method requires identifying these landmarks for each vertebral level, which is time-consuming. In this work, a quasi-automated method for pedicle detection and VAR estimation was proposed.

METHOD

A total of 149 healthy and adolescent idiopathic scoliotic (AIS) subjects were included in this retrospective study. Their frontal X-rays were collected from multiple sites and manually annotated to identify the spinal midline and pedicle positions. Then, an automated pedicle detector was developed based on image analysis, machine learning and fast manual identification of a few landmarks. VARs were calculated using the Stokes method in a validation dataset of 11 healthy (age 6-33 years) and 46 AIS subjects (age 6-16 years, Cobb 10°-46°), both from detected pedicles and those manually annotated to compare them. Sensitivity of pedicle location to the manual inputs was quantified on 20 scoliotic subjects, using 10 perturbed versions of the manual inputs.

RESULTS

Pedicles centers were localized with a precision of 84% and mean difference of 1.2 ± 1.2 mm, when comparing with manual identification. Comparison of VAR values between automated and manual pedicle localization yielded a signed difference of - 0.2 ± 3.4°. The uncertainty on pedicle location was smaller than 2 mm along each image axis.

CONCLUSION

The proposed method allowed calculating VAR values in frontal radiographs with minimal user intervention and robust quasi-automated pedicle localization. These slides can be retrieved under Electronic Supplementary Material.

Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach

PURPOSE

We present an automated method for extracting anatomical parameters from biplanar radiographs of the spine, which is able to deal with a wide scenario of conditions, including sagittal and coronal deformities, degenerative phenomena as well as images acquired with different fields of view.

METHODS

The location of 78 landmarks (end plate centers, hip joint centers, and margins of the S1 end plate) was extracted from three-dimensional reconstructions of 493 spines of patients suffering from various disorders, including adolescent idiopathic scoliosis, adult deformities, and spinal stenosis. A fully convolutional neural network featuring an additional differentiable spatial to numerical (DSNT) layer was trained to predict the location of each landmark. The values of some parameters (T4-T12 kyphosis, L1-L5 lordosis, Cobb angle of scoliosis, pelvic incidence, sacral slope, and pelvic tilt) were then calculated based on the landmarks' locations. A quantitative comparison between the predicted parameters and the ground truth was performed on a set of 50 patients.

RESULTS

The spine shape predicted by the models was perceptually convincing in all cases. All predicted parameters were strongly correlated with the ground truth. However, the standard errors of the estimated parameters ranged from 2.7° (for the pelvic tilt) to 11.5° (for the L1-L5 lordosis).

CONCLUSIONS

The proposed method is able to automatically determine the spine shape in biplanar radiographs and calculate anatomical and posture parameters in a wide scenario of clinical conditions with a very good visual performance, despite limitations highlighted by the statistical analysis of the results. These slides can be retrieved under Electronic Supplementary Material.

Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction

To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE). The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids. Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing. In the CGAdaBoost classifier, diversified vertebrae images and multifeature descriptors are considered to generate more discriminative features, thus improving the vertebral detection accuracy. After that, the detected bounding box represents an appropriate initial contour of DRLSE to make the vertebral segmentation more accurate. It is helpful for the elimination of initialization sensitivity and quick convergence of vertebra boundaries. Meanwhile, vertebral centroids are extracted to connect the whole spine, thereby describing the spinal curvature. Different parts of the spine are determined as abnormal or normal in accordance with medical prior knowledge. The experimental results demonstrate that the proposed method cannot only effectively identify scoliosis with unlabeled spine CT images but also have superiority against other state-of-the-art methods.

Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net

Automated quantitative estimation of spinal curvature is an important task for the ongoing evaluation and treatment planning of Adolescent Idiopathic Scoliosis (AIS). It solves the widely accepted disadvantage of manual Cobb angle measurement (time-consuming and unreliable) which is currently the gold standard for AIS assessment. Attempts have been made to improve the reliability of automated Cobb angle estimation. However, it is very challenging to achieve accurate and robust estimation of Cobb angles due to the need for correctly identifying all the required vertebrae in both Anterior-posterior (AP) and Lateral (LAT) view x-rays. The challenge is especially evident in LAT x-ray where occlusion of vertebrae by the ribcage occurs. We therefore propose a novel Multi-View Correlation Network (MVC-Net) architecture that can provide a fully automated end-to-end framework for spinal curvature estimation in multi-view (both AP and LAT) x-rays. The proposed MVC-Net uses our newly designed multi-view convolution layers to incorporate joint features of multi-view x-rays, which allows the network to mitigate the occlusion problem by utilizing the structural dependencies of the two views. The MVC-Net consists of three closely-linked components: (1) a series of X-modules for joint representation of spinal structure (2) a Spinal Landmark Estimator network for robust spinal landmark estimation, and (3) a Cobb Angle Estimator network for accurate Cobb Angles estimation. By utilizing an iterative multi-task training algorithm to train the Spinal Landmark Estimator and Cobb Angle Estimator in tandem, the MVC-Net leverages the multi-task relationship between landmark and angle estimation to reliably detect all the required vertebrae for accurate Cobb angles estimation. Experimental results on 526 x-ray images from 154 patients show an impressive 4.04° Circular Mean Absolute Error (CMAE) in AP Cobb angle and 4.07° CMAE in LAT Cobb angle estimation, which demonstrates the MVC-Net's capability of robust and accurate estimation of Cobb angles in multi-view x-rays. Our method therefore provides clinicians with a framework for efficient, accurate, and reliable estimation of spinal curvature for comprehensive AIS assessment.

Computer Assisted Cobb Angle Measurements: A novel algorithm

BACKGROUND

The standard for evaluating scoliosis is PA radiographs using Cobb angle to measure curve magnitude. Newer PACS systems allow easier Cobb angle calculations, but have not improved inter/intra observer precision of measurement. Cobb angle and its progression are important to determine treatment; therefore, angle variability is not optimal. This study seeks to demonstrate that a performance equivalent to that achieved in the manual method is possible using a novel computer algorithm with limited user input. The authors compared Cobb angles from predetermined spinal levels in the average attending score versus the computer assisted approach.

METHODS

Retrospective analysis of PA radiographs from 58 patients previously evaluated for scoliosis was collected. Predesignated spinal levels (e.g., T2-T10) were assigned for different curves and calculated by Cobb method. Four spine surgeons evaluated these Cobb angles. Their average scores were measured and compared to formulated values using the novel computer-based algorithm. Literature reports inter-observer reliability is 6.3-7.2degrees. Limits of accuracy were set at 5 degrees of average orthopedic surgeons' score.

RESULTS

The computer-based algorithm calculated Cobb angles within 5 degrees of orthopedic surgeons' average with a standard deviation of 3.2 degrees. This result was based on a 95% confidence interval with p values <0.001. The computer algorithm was plotted against average angle determined by the surgeons, with individual determinations and linear regression (r² =0.90). The average difference between surgeons' measures and computer algorithm was 0.4 degrees(SD= 3.2degrees, n=79). There was a tendency for the computer algorithm program to overestimate the angle at larger angles, but difference was small with r² = 0.09.

CONCLUSIONS

Our study showed the novel computer based algorithm was an efficient and reliable method to assess scoliotic curvature in the coronal plane with the possibility of expediting clinic visits, ensuring reliability of calculation and decreasing patient exposure to radiation. Level of Evidence: III.

Contour and Angle-Function Based Scoliosis Monitoring: Relaxing the Requirement on Image Quality in the Measurement of Spinal Curvature

PURPOSE

A method for measuring spinal curvature that provides a useful analog to the Cobb angle and is tolerant of degraded image quality is proposed. Conventional methods require a higher standard of discernibility for vertebra features and suffer high variability.

METHODS

Assumption is made that the natural representation of the spine for the purpose of scoliosis monitoring is that of a continuous curved contour rather than a series of discrete vertebral bodies with individual orientations. The angle that a tangent line to this contour makes with the vertical, expressed as a continuous function of height, is proposed as a metric for characterization of the curve. The Cobb angle can be approximated as the difference between the extrema of this function, and details of the function shape can provide additional markers for tracking curve variation and evolution. A method for deriving the angle function from coronal images of the spine is proposed, and both manual and automatic variants of the procedure are described.

RESULTS

The method is applied to conventional coronal radiographs and to magnetic resonance (MR) coronal views derived from volumetric acquisitions of the spine. Included in the latter category is an image exhibiting poor discrimination of vertebra features due to motion artifacts. The method permits extraction of the curve and Cobb angles in all cases.

CONCLUSIONS

Because the spine contour is discernible even in low quality images where vertebral endplates may be obscured or poorly contrasted from surrounding tissue, the approach offers improved reliability, applicability across imaging modalities, and, in the case of x-rays, the possibility of a reduced radiation dose. Moreover, since it relies on larger image features and exploits the continuity of the spine, the contour-based approach is expected to reduce the variability associated with Cobb angle measurement.

APPLICABILITY OF THE COBB ANGLE MEASUREMENT IN IDIOPATHIC SCOLIOSIS USING SCANNED IMAGING

ABSTRACT Objectives: To compare the measurement of the Cobb angle on printed radiographs and on scanned radiographs viewed through the software "PixViewer". Methods: Preoperative radiographs of 23 patients were evaluated on printed films and through the software "PixViewer". The same evaluator, a spine surgeon, chose the proximal and distal limiting vertebrae of the main curve on printed radiographs, without identification of patients, and measured the Cobb angle based on these parameters. The same parameters and measurements were applied to scanned radiographs. The measurements were compared, as well as the choice of limiting vertebrae. Results: The average variation of the Cobb angle between methods was 1.48 ± 1.73°. The intraclass correlation coefficient (ICC) was 0.99, demonstrating excellent reproducibility. Conclusion: The Cobb method can be used to evaluate scoliosis through the "PixViewer" tool with the same reliability as the classic method on printed radiographs.

Reliability of a novel Cobb protractor for measuring the Cobb angle of radiograph in scoliosis

OBJECTIVE

To introduce a novel Cobb protractor and assess its reliability and rapidity for measuring Cobb angle in scoliosis patients.

METHODS

The novel Cobb protractor had two endplate markers. A measurement was performed just to align the two markers to each endplate of the curve. The Cobb angle on the posteroanterior radiographs of 24 patients clinically diagnosed with adolescent idiopathic scoliosis was measured by three orthopedic surgeons with both standard Cobb method and the new technique, and the time of measurement was recorded. Intraclass correlation coefficients (ICCs) were calculated to assess the reliability of the new method.

RESULTS

The time for a measurement with the new tool was approximately 10 seconds less than the time that used to finish a measurement with the standard method (P<0.05). The overall mean Cobb angle for the major curve of the 24 patients was 47.8°. The mean overall intraobserver and interobserver ICC was 0.971 and 0.971 for the Cobb method group, while the overall intraobserver ICC and the interobserver was 0.985 and 0.979 for the new tool group.

CONCLUSIONS

The novel Cobb protractor could perform quick measurement and measure almost all forms of radiographs. The Cobb protractor might be an ideal instrument to measure the Cobb angle.

Correlation between Cobb angle, spinous process angle (SPA) and apical vertebrae rotation (AVR) on posteroanterior radiographs in adolescent idiopathic scoliosis (AIS)

PURPOSE

To investigate the accuracy and reliability of the Cobb angle, the spinous process angle (SPA), and apical vertebral rotation (AVR) for measuring adolescent idiopathic scoliosis (AIS), and to evaluate the correlations between these measurements.

METHODS

A retrospective study of two sets of standing posteroanterior radiographs of patients with AIS was performed. The first set was 59 consecutive patients with AIS with Cobb angles <45° and the second set was 25 patients with Cobb angles >45°. The Cobb angle, SPA and AVR of each curve was measured twice by three observers with varying measurement experience. The mean absolute difference, standard deviation, and intra- and inter-rater reliability coefficients for each measurement were examined. The Pearson correlation coefficients between any two parameters were reported. The association of the Cobb angle with the SPA and AVR was examined using a multiple regression model.

RESULTS

The average intra- and inter-observer reliabilities (ICC [2, 1]) of the Cobb angle, SPA, and AVR were 0.99, 0.95, 0.92 and 0.98, 0.88, 0.83, respectively. The correlation coefficients (r) between Cobb angle and SPA, Cobb angle and AVR, and SPA and AVR were 0.93, 0.68, and 0.60, respectively. Using multiple regression, the association between the Cobb angle and SPA combined with AVR was R (2) = 0.90. The resulting regression model was: [Formula: see text].

CONCLUSION

The SPA has high correlation with the Cobb angle. Including the AVR as an additional factor in multiple regression improves the prediction of the Cobb angle.

Reliable and reproducible classification system for scoliotic radiograph using image processing techniques

Scoliosis classification is useful for guiding the treatment and testing the clinical outcome. State-of-the-art classification procedures are inherently unreliable and non-reproducible due to technical and human judgmental error. In the current diagnostic system each examiner will have diagrammatic summary of classification procedure, number of scoliosis curves, apex level, etc. It is very difficult to define the required anatomical parameters in the noisy radiographs. The classification system demands automatic image understanding system. The proposed automated classification procedures extracts the anatomical features using image processing and applies classification procedures based on computer assisted algorithms. The reliability and reproducibility of the proposed computerized image understanding system are compared with manual and computer assisted system using Kappa values.

Computer-assisted radiographic calculation of spinal curvature in brachycephalic "screw-tailed" dog breeds with congenital thoracic vertebral malformations: reliability and clinical evaluation

The objectives of this study were: To investigate computer-assisted digital radiographic measurement of Cobb angles in dogs with congenital thoracic vertebral malformations, to determine its intra- and inter-observer reliability and its association with the presence of neurological deficits. Medical records were reviewed (2009-2013) to identify brachycephalic screw-tailed dog breeds with radiographic studies of the thoracic vertebral column and with at least one vertebral malformation present. Twenty-eight dogs were included in the study. The end vertebrae were defined as the cranial end plate of the vertebra cranial to the malformed vertebra and the caudal end plate of the vertebra caudal to the malformed vertebra. Three observers performed the measurements twice. Intraclass correlation coefficients were used to calculate the intra- and inter-observer reliabilities. The intraclass correlation coefficient was excellent for all intra- and inter-observer measurements using this method. There was a significant difference in the kyphotic Cobb angle between dogs with and without associated neurological deficits. The majority of dogs with neurological deficits had a kyphotic Cobb angle higher than 35°. No significant difference in the scoliotic Cobb angle was observed. We concluded that the computer assisted digital radiographic measurement of the Cobb angle for kyphosis and scoliosis is a valid, reproducible and reliable method to quantify the degree of spinal curvature in brachycephalic screw-tailed dog breeds with congenital thoracic vertebral malformations.

Intra- and Interobserver Reliability of the Cobb Angle-Vertebral Rotation Angle-Spinous Process Angle for Adolescent Idiopathic Scoliosis

STUDY DESIGN

A reliability analysis of Cobb angle, vertebral rotation (VR), and spinous process angle (SPA) measurements in adolescent idiopathic scoliosis.

OBJECTIVE

To determine the intra- and interobserver reliability of semi-automated digital radiograph measurements.

SUMMARY OF BACKGROUND DATA

Cobb angle measurements on posteroanterior radiographs are commonly used to determine the severity of scoliosis. Vertebral rotation helps assess scoliosis 3-dimensionally and has a role in predicting curve progression. Recent studies have shown that the spinous process angle is a useful parameter in assessing scoliosis when using ultrasound imaging. Because the reliability of SPA measurements on radiographs has yet to be determined, it is important to compare the reliability of these 3 parameters (Cobb angle, VR, and SPA) using a computer assisted semi-automated method.

METHODS

Sixty posteroanterior radiographs of patients with adolescent idiopathic scoliosis were obtained and measured twice by 3 observers who were blinded to their previous measurements, using an in-house developed program. Measurements were obtained using a semi-automated method to minimize variability resulting from observer reliability. The intra- and interobserver reliabilities were analyzed using intra-class correlation coefficients (ICCs) as well as Bland-Altman's bias and limits of agreement.

RESULTS

Over 350 (intra) and 90 (inter) sets of curves with an average Cobb angle of 26° ± 9° (range, 10° to 44°) were compared for each parameter. Intra-observer reliabilities for each parameter were excellent (ICC_[2,1], .82; 1.00), with mean absolute differences under 3° between most measurements. Interobserver reliability (ICC_[2,1], .72; .95) was mostly good to excellent, with mean absolute differences ranging from 2.0° to 5.6°.

CONCLUSIONS

Both the intra- and interobserver assessment of the Cobb, VR, and SPA from the semi-automated measurements had clinically acceptable reliability ranges and may be considered for clinical implementation. Additional studies will be conducted to determine the accuracy and sensitivity to change of these scoliosis severity measurements.

Manual and computerized measurement of coronal vertebral inclination on MRI images: a pilot study

AIM

A pilot study that presents a systematic approach for evaluating the variability of manual and computerized measurements of coronal vertebral inclination (CVI) on images acquired by magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Three observers identified the vertebral body corners of 28 vertebrae on two occasions on two-dimensional (2D) coronal MRI cross-sections, which served to evaluate CVI using six manual measurements (superior and inferior tangents, left and right tangents, mid-endplate and mid-wall lines). Computerized measurements were performed by evaluating CVI from the symmetry of vertebral anatomical structures of the same 28 vertebrae in 2D coronal MRI cross-sections and in three-dimensional (3D) MRI images.

RESULTS

In terms of standard deviation (SD), the mid-endplate lines proved to be the manual measurements with the lowest intra- (1.0° SD) and interobserver (1.4° SD) variability. The computerized measurements in 3D yielded even lower intra- (0.8° SD) and interobserver (1.3° SD) variability. The strongest inter-method agreement (1.2° SD) was found among lines parallel to vertebral endplates (superior tangents, inferior tangents, mid-endplate lines). The computerized measurements in 3D were most in agreement with the mid-endplate lines (1.9° SD). The estimated intra- and interobserver variabilities of standard Cobb angle measurements were equal to 1.6° SD and 2.5° SD, respectively, for manual measurements, and to 1.1° SD and 1.8° SD, respectively, for computerized measurements.

CONCLUSION

The mid-endplate lines proved to be the most reproducible and reliable manual CVI measurements. Computerized CVI measurements based on the evaluation of the symmetry of vertebral anatomical structures in 3D were more reproducible and reliable than manual measurements.

Measuring procedures to determine the Cobb angle in idiopathic scoliosis: a systematic review

BACKGROUND

Scoliosis of the vertebral column can be assessed with the Cobb angle (Cobb 1948). This examination is performed manually by measuring the angle on radiographs and is considered the gold standard. However, studies evaluating the reproducibility of this procedure have shown high variability in intra- and inter-observer agreement. Because of technical advancements, interests in new procedures to determine the Cobb angle has been renewed. This review aims to systematically investigate the reproducibility of various new techniques to determine the Cobb angle in idiopathic scoliosis and to assess whether new technical procedures are reasonable alternatives when compared to manual measurement of the Cobb angle.

METHOD

Systematic review. Studies examining procedures used to determine the Cobb angle were selected. Two review authors independently selected studies for inclusion, extracted data and assessed risk of bias. Statistical results of reliability and agreement were summarised and described.

RESULTS

Eleven studies of new measuring procedures were included, all reporting the reproducibility. The new procedures can be divided into computer-assisted procedures, automatic procedures and smartphone apps.

CONCLUSIONS

All investigated measuring procedures showed high degrees of reliability. In general, digital procedures tend to be slightly better than manual ones. For all other measurement procedures (automatic or smartphone), results varied. Studies implementing vertebral pre-selection and observer training achieved better agreement.