Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.IRSC / CIH

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.IRSC / CIH

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.CIHR / IRS

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.CIHR / IRS

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.CIHR (Canadian Institutes of Health Research

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

Collection : Lecture notes in computer science ; vol. 6112In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.CIHR / IRS

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

Modélisation physique des tissus mous du tronc scoliotique pour la simulation de l'apparence post-chirurgicale

RÉSUMÉ La scoliose idiopathique de l'adolescence (SIA) est une déformation tridimensionnelle complexe de la colonne vertébrale et de la cage thoracique. Dans le cas de déformation sévère, le recours à la chirurgie correctrice de la colonne vertébrale est requis comme moyen de traitement. Environ un patient sur mille atteint de SIA aura à subir une chirurgie correctrice de la colonne vertébrale. Cependant, dans la plupart des cas, une correction optimale de la colonne vertébrale n'entraine pas nécessairement une correction optimale de l'apparence externe. Une asymétrie du tronc peut persister à l'issue de la chirurgie et cela est difficile à prédire par les chirurgiens. Cela est problématique, car l'apparence externe est un facteur important de satisfaction pour les patients. Il serait intéressant de disposer d'outils d'assistance à la planification de chirurgie pour la scoliose prenant en compte les attentes du patient concernant l'esthétique de l'apparence du tronc. La simulation médicale sur ordinateur est devenue un outil important d'assistance à la prise de décision clinique. Elle est utilisée pour permettre de prédire et analyser les effets de traitements médicaux, ainsi que la prédiction de changements anatomiques dus à l'évolution d'une pathologie. Dans le contexte de la chirurgie pour la scoliose, des simulateurs de chirurgie correctrice de la colonne vertébrale existent. Des modèles biomécaniques pour la simulation de l'instrumentation de la colonne vertébrale en chirurgie de la scoliose ont été développés par différents chercheurs. Toutefois, ceux-ci ne prédisent pas la forme externe du tronc après chirurgie. De cet état des choses, découle la problématique et les objectifs de cette thèse: modéliser le tronc scoliotique en vue de la simulation de la forme postopératoire du tronc, et améliorer la précision des prédictions afin de proposer une stratégie opératoire optimale. La question de recherche abordée dans cette thèse concerne le développement de méthodes pour la simulation et la prédiction de la forme post-opératoire du tronc en chirurgie pour la scoliose. Quatre objectifs spécifiques de recherche ont été définis. La première partie du travail (traitant du premier objectif) a consisté à développer un modèle physique de déformation pour le tronc scoliotique. Contrairement au modèle existant, un nouveau modèle physique de déformation incrémentale est proposé pour tenir compte des grandes déformations du tronc. L'inspection qualitative des surfaces de tronc simulées et réelles montre une bonne approximation de la correction de la gibbosité. L'évaluation quantitative de la simulation est basée sur l'indice de rotation de la surface du dos (indice BSR). Il se définit comme l'angle formé par la double tangente du côté postérieur de chaque section horizontale de la surface du tronc et l'axe passant par les épines iliaques antéro-supérieures (ASIS) projeté sur le plan frontal. Les valeurs d'indices BSR, mesurées à différents niveaux vertébraux, montrent une erreur moyenne de 1.20º (± 0.73) à 3.20º (± 0.83) dans la région thoracique, indiquant un accord entre les troncs prédits et les données réelles. La deuxième partie (regroupant les trois autres objectifs spécifiques) a consisté à améliorer la précision des prédictions. Nous proposons deux méthodes de détermination de formes à priori de tronc postopératoire (soit basé sur une prédiction statistique, soit basé sur une prédiction de type proche voisin). Ces outils exploitent l'intuition de choisir la restriction du champ de déplacement à la frontière du domaine du tronc (la surface externe) comme une première approximation de la déformation du tronc. La réalisation des objectifs de cette recherche est à l'origine de contributions originales à l'état de l'art aussi bien en simulation physique de tissus mous qu'en apprentissage machine pour l'analyse de formes. Ce projet propose une nouvelle méthode de modélisation des déformations de tissus mous du tronc scoliotique pour la simulation de l'apparence postopératoire. Cette méthode présente, ainsi, l'avantage de constituer un outil pour les systèmes de planification par ordinateur de traitement chirurgical de la scoliose. En perspective, des études complémentaires sont suggérées pour surmonter certaines limitations des méthodes proposées. En particulier, l'incorporation d'un modèle du tronc obtenu par une fusion multimodale d'images (IRM/RX/TOPO) de patients scoliotiques, pour une meilleure personnalisation géométrique, devrait conduire à une amélioration de la précision de la simulation.----------ABSTRACT Adolescent Idiopathic scoliosis (AIS) is a complex three-dimensional deformation of the spine and rib cage. In case of severe spine deformity, a spine surgery is required as a treatment. Approximately one in a thousand patients suffering from AIS will have a spine surgery. However, in most cases, an optimal correction of the spine does not necessarily results in an optimal correction of the external appearance. A trunk asymmetry may persist after surgery and it is difficult to predict by surgeons. This is problematic because the external appearance is one of the most important factor for the patient satisfaction. It would be interesting to have available computer based scoliosis surgery planning assistance tools that takes into account the expectation of the patient regarding the aesthetics of the trunk appearance. Computer based medical simulation is becoming an important tool to support clinical decision making. It is used to predict and analyze the effects of treatments, as well as the predictions of changes due to pathology evolution. In the context of scoliosis surgery, spine correction surgery simulators exist. Biomechanical models for the simulation of the spine instrumentation in scoliosis surgery have been developed by different researchers. However, they do not simulate the postoperative appearance of the trunk. From this observation arise the problem and objectives of this thesis: modeling the scoliotic trunk in order to simulate the postoperative trunk shape, and improve predictions accuracy in order to propose an optimal surgery strategy. The research question of this thesis concerns the development of methods for the simulation and the prediction of the trunk postoperative shape in scoliosis surgery. Four research objectives have been defined. The first part of this work (dealing with the first objective) consisted in developing a physically based deformation model of the scoliotic trunk. Unlike the existing model, a novel incremental approach is proposed to take into account large deformations of the trunk. The qualitative visual inspection of the simulated and actual trunk surfaces show a good approximation of the correction of the rib hump. The quantitative evaluation of the simulation is based on the back surface rotation index (BSR index). It is defined as the angle formed by the dual tangent to the posterior side of each section of the trunk surface and the axis passing through the patient's anterior superior iliac spines (ASIS), projected onto the axial plane. The BSR indices, measured at different vertebral levels, shows an average error in the range of 1.20º (± 0.73) to 3.20º (± 0.83$) in the thoracic region, indicating a good agreement between the predicted and actual trunk surfaces. The second part (dealing with the remaining three objectives) addressed the prediction accuracy improvement. In this regard, two tools have been developed: one for predicting 3D trunk shapes based on a statistical approach, and the other being a prediction tool based on nearest neighbor methods. These tools make use of the intuition of choosing the restriction of the displacement field on the trunk domain boundary (the external surface) as a first approximation of the trunk deformation. The achievement of the research objectives has resulted in original contributions to the state of the art in physical simulation of soft tissues as well as in machine learning for shape analysis. This project proposes a novel method for modeling scoliotic trunk soft tissue deformation for the simulation of the postoperative appearance. This method has, thus, the advantage of being a potential tool for computer based scoliosis surgery planning systems. As perspectives, further research studies may be suggested in order to overcome the limitations of the proposed methods. In particular, the incorporation of a trunk model obtained from a multimodal image fusion (MRI / RX / TOPO) for a better personalization of the physical constants may lead to the improvement of the simulation accuracy

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance