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

A Cloud robotics architecture to foster individual child partnership in medical facilities

Robots and automation systems have become a valuable partner in several facets of human life: from learning and teaching, to daily working, including health monitoring and assistance. So far, these appealing robot-based applications are restricted to conduct repetitive, yet useful, tasks due to the reduced individual robots’ capabilities in terms of processing and computation. This concern prevents current robots from facing more complex applications related to understanding hu- man beings and perceiving their subtle feelings. Such hardware limitations have been already found in the computer science field. In this domain, they are currently being addressed using a new resource exploitation model coined as cloud computing, which is targeted at enabling massive storage and computation using smartly connected and inexpensive commodity hardware. The purpose of this paper is to propose a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, this paper presents a multi-agent learning system that combines machine learning and cloud computing using low-cost robots to (1) collect and perceive children status, (2) build a human-readable set of rules related to the child-robot relationship, and (3) improve the children experience during their stay in the hos- pital. Conducted preliminary experiments proof the feasibility of this proposal and encourage practitioners to work towards this direction.Peer ReviewedPostprint (published version

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

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

Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

A contribution to the incorporation of sociability and creativity skills to computers and robots

This dissertation contains the research and work completed by the PhD candidate on the incorporation of sociability and creativity skills to computers and robots. Both skills can be directly related with empathy, which is the ability to understand and share the feelings of another. In this form, this research can be contextualized in the framework of recent developments towards the achievement of empathy machines. The first challenge at hands refers to designing pioneering techniques based on the use of social robots to improve user experience interacting with them. In particular, research focus is on eliminating or minimizing pain and anxiety as well as loneliness and stress of long-term hospitalized child patients. This challenge is approached by developing a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, a multiagent learning system is introduced based on a combination of machine learning and cloud computing using low-cost robots (Innvo labs's Pleo rb). Moreover, a wireless communication system is also developed for the Pleo robot in order to help the health professional who conducts therapy with the child, monitoring, understanding, and controlling Pleo behavior at any moment. As a second challenge, a new formulation of the concept of creativity is proposed in order to empower computers with. Based on previous well established theories from Boden and Wiggins, this thesis redefines the formal mechanism of exploratory and transformational creativity in a way which facilitates the computational implementation of these mechanisms in Creativity Support Systems. The proposed formalization is applied and validated on two real cases: the first, about chocolate designing, in which a novel and flavorful combination of chocolate and fruit is generated. The second case is about the composition of a single voice tune of reel using ABC notation.Postprint (published version

A contribution to the incorporation of sociability and creativity skills to computers and robots

This dissertation contains the research and work completed by the PhD candidate on the incorporation of sociability and creativity skills to computers and robots. Both skills can be directly related with empathy, which is the ability to understand and share the feelings of another. In this form, this research can be contextualized in the framework of recent developments towards the achievement of empathy machines. The first challenge at hands refers to designing pioneering techniques based on the use of social robots to improve user experience interacting with them. In particular, research focus is on eliminating or minimizing pain and anxiety as well as loneliness and stress of long-term hospitalized child patients. This challenge is approached by developing a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, a multiagent learning system is introduced based on a combination of machine learning and cloud computing using low-cost robots (Innvo labs's Pleo rb). Moreover, a wireless communication system is also developed for the Pleo robot in order to help the health professional who conducts therapy with the child, monitoring, understanding, and controlling Pleo behavior at any moment. As a second challenge, a new formulation of the concept of creativity is proposed in order to empower computers with. Based on previous well established theories from Boden and Wiggins, this thesis redefines the formal mechanism of exploratory and transformational creativity in a way which facilitates the computational implementation of these mechanisms in Creativity Support Systems. The proposed formalization is applied and validated on two real cases: the first, about chocolate designing, in which a novel and flavorful combination of chocolate and fruit is generated. The second case is about the composition of a single voice tune of reel using ABC notation

Abstracts of the College of Surgeons of East, Central and Southern Africa (COSECSA) 19th Annual General Meeting and International Scientific Conference

Conference Abstract