Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Objective. To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods. A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results. For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion. The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance. Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis

Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation

Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, demonstrating the reliability of this process in both vertebra localization and Cobb angle measurement

Automation of Spine Curve Assessment in Frontal Radiographs Using Deep Learning of Vertebral-tilt Vector

In this paper, an automated and visually explainable system is proposed for a scoliosis assessment from spinal radiographs, which deals with the drawback of manual measurements, which are known to be time-consuming, cumbersome, and operator dependent. Deep learning techniques have been successfully applied in the accurate extraction of Cobb angle measurements, which is the gold standard for a scoliosis assessment. Such deep learning methods directly estimate the Cobb angle without providing structural information of the spine which can be used for diagnosis. Although conventional segmentationbased methods can provide the spine structure, they still have limitations in the accurate measurement of the Cobb angle. It would be desirable to build a clinician-friendly diagnostic system for scoliosis that provides not only an automated Cobb angle assessment but also local and global structural information of the spine. This paper addresses this need through the development of a hierarchical method which consisting of three major parts. (1) A confidence map is used to selectively localize and identify all vertebrae in an accurate and robust manner, (2) vertebral-tilt field is used to estimate the slope of an individual vertebra, and (3) the Cobb angle is determined by combining the vertebral centroids with the previously obtained vertebral-tilt field. The performance of the proposed method was validated, resulting in circular mean absolute error of 3:51 and symmetric mean absolute percentage error of 7:84% for the Cobb angle.ope

Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods

IntroductionIn light of the potential problems of missed diagnosis and misdiagnosis in the diagnosis of spinal diseases caused by experience differences and fatigue, this paper investigates the use of artificial intelligence technology for auxiliary diagnosis of spinal diseases.MethodsThe LableImg tool was used to label the MRIs of 604 patients by clinically experienced doctors. Then, in order to select an appropriate object detection algorithm, deep transfer learning models of YOLOv3, YOLOv5, and PP-YOLOv2 were created and trained on the Baidu PaddlePaddle framework. The experimental results showed that the PP-YOLOv2 model achieved a 90.08% overall accuracy in the diagnosis of normal, IVD bulges and spondylolisthesis, which were 27.5 and 3.9% higher than YOLOv3 and YOLOv5, respectively. Finally, a visualization of the intelligent spine assistant diagnostic software based on the PP-YOLOv2 model was created and the software was made available to the doctors in the spine and osteopathic surgery at Guilin People's Hospital.Results and discussionThis software automatically provides auxiliary diagnoses in 14.5 s on a standard computer, is much faster than doctors in diagnosing human spines, which typically take 10 min, and its accuracy of 98% can be compared to that of experienced doctors in the comparison of various diagnostic methods. It significantly improves doctors' working efficiency, reduces the phenomenon of missed diagnoses and misdiagnoses, and demonstrates the efficacy of the developed intelligent spinal auxiliary diagnosis software

A convolutional neural network to detect scoliosis treatment in radiographs

Purpose The aim of this work is to propose a classification algorithm to automatically detect treatment for scoliosis (brace, implant or no treatment) in postero-anterior radiographs. Such automatic labelling of radiographs could represent a step towards global automatic radiological analysis. Methods Seven hundred and ninety-six frontal radiographies of adolescents were collected (84 patients wearing a brace, 325 with a spinal implant and 387 reference images with no treatment). The dataset was augmented to a total of 2096 images. A classification model was built, composed by a forward convolutional neural network (CNN) followed by a discriminant analysis; the output was a probability for a given image to contain a brace, a spinal implant or none. The model was validated with a stratified tenfold cross-validation procedure. Performance was estimated by calculating the average accuracy. Results 98.3% of the radiographs were correctly classified as either reference, brace or implant, excluding 2.0% unclassified images. 99.7% of brace radiographs were correctly detected, while most of the errors occurred in the reference group (i.e. 2.1% of reference images were wrongly classified). Conclusion The proposed classification model, the originality of which is the coupling of a CNN with discriminant analysis, can be used to automatically label radiographs for the presence of scoliosis treatment. This information is usually missing from DICOM metadata, so such method could facilitate the use of large databases. Furthermore, the same model architecture could potentially be applied for other radiograph classifications, such as sex and presence of scoliotic deformity.Acknowledgements The authors are grateful to the ParisTech BiomecAM chair program on subject-specific musculoskeletal modelling (with the support of ParisTech and Yves Cotrel Foundations, Société Générale, Proteor and Covea)

Identifying the Severity of Adolescent Idiopathic Scoliosis During Gait by Using Machine Learning

La scoliose idiopathique de l'adolescent (SIA) est une déformation de la colonne vertébrale dans les trois plans de l’espace objectivée par un angle de Cobb ≥ 10°. Celle-ci affecte les adolescents âgés entre 10 et 16 ans. L’étiologie de la scoliose demeure à ce jour inconnue malgré des recherches approfondies. Différentes hypothèses telles que l’implication de facteurs génétiques, hormonaux, biomécaniques, neuromusculaires ou encore des anomalies de croissance ont été avancées. Chez ces adolescents, l'ampleur de la déformation de la colonne vertébrale est objectivée par mesure manuelle de l’angle de Cobb sur radiographies antéropostérieures. Cependant, l’imprécision inter / intra observateur de cette mesure, ainsi que de l’exposition fréquente (biannuelle) aux rayons X que celle-ci nécessite pour un suivi adéquat, sont un domaine qui préoccupe la communauté scientifique et clinique. Les solutions proposées à cet effet concernent pour beaucoup l'utilisation de méthodes assistées par ordinateur, telles que des méthodes d'apprentissage machine utilisant des images radiographiques ou des images du dos du corps humain. Ces images sont utilisées pour classer la sévérité de la déformation vertébrale ou pour identifier l'angle de Cobb. Cependant, aucune de ces méthodes ne s’est avérée suffisamment précise pour se substituer l’utilisation des radiographies. Parallèlement, les recherches ont démontré que la scoliose modifie le schéma de marche des personnes qui en souffrent et par conséquent également les efforts intervertébraux. C’est pourquoi, l'objectif de cette thèse est de développer un modèle non invasif d’identification de la sévérité de la scoliose grâce aux mesures des efforts intervertébraux mesurés durant la marche. Pour atteindre cet objectif, nous avons d'abord comparé les efforts intervertébraux calculés par un modèle dynamique multicorps, en utilisant la dynamique inverse, chez 15 adolescents atteints de SIA avec différents types de courbes et de sévérités et chez 12 adolescents asymptomatiques (à titre comparatif). Par cette comparaison, nous avons pu objectiver que les efforts intervertébraux les plus discriminants pour prédire la déformation vertébrale étaient la force et le couple antéro-postérieur et la force médio-latérale. Par la suite, nous nous sommes concentrés sur la classification de la sévérité de la déformation vertébrale de 30 AIS ayant une courbure thoraco-lombaire / lombaire. Pour ce faire, nous avons testé différents modèles de classification. L'angle de Cobb a été identifié en exécutant différents modèles de régression. Les caractéristiques (features) servant à alimenter les algorithmes d'entraînement ont été choisies en fonction des efforts intervertébraux les plus pertinents à la déformation vertébrale au niveau de la charnière lombo-sacrée (vertèbres allantes de L5-S1). Les précisions les plus élevées pour la classification exécutant différents algorithmes ont été obtenues par un algorithme de classification d'ensemble comprenant les “K-nearest neighbors”, “Support vector machine”, “Random forest”, “multilayer perceptron”, et un modèle de “neural networks” avec une précision de 91.4% et 93.6%, respectivement. De même, le modèle de régression par “Decision tree” parmi les autres modèles a obtenu le meilleur résultat avec une erreur absolue moyenne égale à 4.6° de moyenne de validation croisée de 10 fois. En conclusion, nous pouvons dire que cette étude démontre une relation entre la déformation de la colonne vertébrale et les efforts intervertébraux mesurés lors de la marche. L'angle de Cobb a été identifié à l'aide d'une méthode sans rayonnement avec une précision prometteuse égale à 4.6°. Il s’agit d’une amélioration majeure par rapport aux méthodes précédemment proposées ainsi que par rapport à la mesure classique réalisée par des spécialistes présentant une erreur entre 5° et 10° (ceci en raison de la variation intra/inter observateur). L’algorithme que nous vous présentons peut être utilisé comme un outil d'évaluation pour suivre la progression de la scoliose. Il peut être considéré comme une alternative à la radiographie. Des travaux futurs devraient tester l'algorithme et l’adapter pour d’autres formes de SIA, telles que les scolioses lombaire ou thoracolombaire.----------ABSTRACT Adolescent idiopathic scoliosis (AIS) is a 3D deformation of the spine and rib cage greater than 10° that affects adolescents between the ages of 10 and 16 years old. The true etiology is unknown despite extensive research and investigation. However, different theories such as genetic and hormonal factors, growth abnormalities or biomechanical and neuromuscular reasons have been proposed as possible causes. The magnitude of spinal deformity in AIS is measured by the Cobb angle in degrees as the gold standard through the X-rays by specialists. The inter/intra observer error and the cumulative exposure to radiation, however, are sources of increasing concern among researchers with regards to the accuracy of manual measurement. Proposed solutions have therefore, focused on using computer-assisted methods such as Machine Learning using X-ray images, and/or trunk images to classify the severity of spinal deformity or to identify the Cobb angle. However, none of the proposed methods have shown the level of accuracy required for use as an alternative to X-rays. Meanwhile, scoliosis has been recognized as a pathology that modifies the gait pattern, subsequently impinging upon intervertebral efforts. The present thesis aims to develop a radiation-free model to identify the severity of idiopathic scoliosis in adolescents based on the intervertebral efforts during gait. To accomplish this objective, we compared the intervertebral efforts computed using a multibody dynamics model, by way of inverse dynamics, among 15 adolescents with AIS having different curve types and severities, as well as 12 typically developed adolescents. This resulted in the identification of the most relevant intervertebral efforts influenced by spinal deformity: mediolateral (ML) force; anteroposterior (AP) force; and torque. Additionally, we focused on the classification of the severity of spinal deformity among 30 AIS with thoracolumbar/lumbar curvature, testing different classification models. Lastly, the Cobb angle was identified running regression models. The features to feed training algorithms were chosen based on the most relevant intervertebral efforts to the spinal deformity on the lumbosacral (L5-S1) joint. The highest accuracies for the classification were obtained by the ensemble classifier algorithm, including “K-nearest neighbors”, “support vector machine”, “random forest”, and “multilayer perceptron”, as well as a neural network model with an accuracy of 91.4% and 93.6%, respectively. Likewise, the “decision tree regression” model achieved the best result with a mean absolute error equal to 4.6 degrees of an averaged 10-fold cross-validation. This study shows a relation between spinal deformity and the produced intervertebral efforts during gait. The Cobb angle was identified using a radiation-free method with a promising accuracy, providing a mean absolute error of 4.6°. Compared to measurement variations, ranging between 5° and 10° in the manual Cobb angle measurements by specialists, the proposed model provided reliable accuracy. This algorithm can be used as an assessment tool, alternative to the X-ray radiography, to follow up the progression of scoliosis. As future work, the algorithm should be tested and modified on AIS with other types of spine curvature than lumbar/thoracolumbar

From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis

Image analysis based on machine learning has gained prominence with the advent of deep learning, particularly in medical imaging. To be effective in addressing challenging image analysis tasks, however, conventional deep neural networks require large corpora of annotated training data, which are unfortunately scarce in the medical domain, thus often rendering fully-supervised learning strategies ineffective.This thesis devises for use in a variety of medical image analysis applications a series of novel deep learning methods, ranging from fully-supervised, single-task learning to scarcely-supervised, multi-task learning that makes efficient use of annotated training data. Specifically, its main contributions include (1) fully-supervised, single-task learning for the segmentation of pulmonary lobes from chest CT scans and the analysis of scoliosis from spine X-ray images; (2) supervised, single-task, domain-generalized pulmonary segmentation in chest X-ray images and retinal vasculature segmentation in fundoscopic images; (3) largely-unsupervised, multiple-task learning via deep generative modeling for the joint synthesis and classification of medical image data; and (4) partly-supervised, multiple-task learning for the combined segmentation and classification of chest and spine X-ray images

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number