Effect of collagen network orientation on the cervical intervertebral disc response to flexion load

National audienc

Prevalence of overweight/obesity and its associated factors among a sample of Moroccan type 2 diabetes patients

Background: Obesity constitutes a major risk factor for the development of diabetes, and has been linked with poor glycaemic control among type 2 diabetic patients. Aims: This study examines the prevalence of overweight/obesity and associated factors in type 2 diabetic patients in the Beni-Mellal Khenifra region in Morocco. Methods: A questionnaire-based cross-sectional study was conducted in 2017 among 975 diabetes patients attending primary health centres. Demographic and clinical data were collected through face-to-face interviews. Anthropometric measurements, including body weight, height and waist circumference, were taken using standardized techniques and calibrated equipment. Results: The prevalence of overweight was 40.4%, the general obesity was 28.8% and the abdominal obesity was 73.7%. Using multivariate analysis, we noted that the general obesity was associated with female sex (AOR= 3,004, 95% CI: 1.761-5.104, P<0.001), increased age (AOR=2.192, 95% CI: 1.116-4.307, P<0.023) and good glycaemic control (AOR=1.594, 95% CI: 1.056-2.407, P=0.027), whereas abdominal obesity was associated wih female sex (AOR=2.654, 95% CI: 1.507-4.671, P<0.001) and insulin treatment (AOR=2.927, 95% CI: 1.031-8.757, P=0.048). Conclusion: Overweight, general obesity and abdominal obesity were high among participants, especially among women. Taken together, these findings urge the implementation of a roadmap for this diabetic subpopulation to have a new lifestyle

Myopia management attitudes in children in clinical practice, towards an innovative and environmental study in Morocco

Myopia is a global public health problem due to its increasing prevalence. Thus, there is growing interest in its early prevention. However, there is a lack of information on the interventions adopted by visual health professionals in Morocco for the management of myopia in children. This study aims to assess their knowledge and raise their awareness of the impact of environmental factors likely to influence the risk of myopia progression. To achieve these objectives, an online survey was distributed to eye care specialists across the country, including a questionnaire assessing their mastery of the different methods available for treating myopia, their level of concern about its development in children, and their opinion on the impact of environmental factors on its onset and growth. The results indicate that most of the professionals consulted are concerned about this pandemic. However, they currently only offer single vision lenses and soft contact lenses, indicating the need for professional training aimed at educating specialists in clinical approaches to myopia management. This would encourage them to adopt alternative solutions for managing myopic children, and to pay particular attention to the environmental factors that influence the onset and progression of myopia

Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19

Modeling of the intervertebral disc biomechanical behavior

La dégénérescence des disques intervertébraux (DD) est un processus naturel qui touche une grande partie de la population. Toutefois, elle peut devenir pathologique et ainsi être accélérée par de multiples facteurs et conduire à un dysfonctionnement précoce des disques lombaires. La DD est un phénomène asymptomatique, ce qui le rend délicat à diagnostiquer précocement. Dans ce cadre, le présent travail vise à développer une méthodologie de diagnostic de la DD utilisant imagerie médicale et modélisation biomécanique.Les disques intervertébraux (DIV) sont des articulations fibro-cartilagineuses qui lient les vertèbres du rachis entre elles. Ils ont pour rôle de supporter et redistribuer les chargements appliqués au rachis tout en assurant sa mobilité. Le DIV peut être assimilé d’un point de vue mécanique à un milieu poreux biologique dont le squelette est formé par la matrice extracellulaire (MEC) et la phase fluide par son contenu hydrique. C’est un milieu hétérogène et anisotrope. Possédant une pression osmotique interne élevée, les DIV ne sont pas vascularisés et possèdent un contenu hydrique important dans lesquels les nutriments cellulaires diffusent à partir des vertèbres adjacentes. Cette particularité rend le fonctionnement des disques très dépendant de la régulation en eau en particulier lorsque ces derniers sont soumis à des sollicitations mécaniques non physiologiques.L’une des clés dans l’élaboration d’un diagnostic fiable de la DD peut être de déterminer de manière objective certaines des caractéristiques internes au disque relatives à son hydratation. Dans le présent travail, une méthodologie de diagnostic quantitatif de la viabilité discale et plus largement de la DD est ainsi proposée. Cette méthodologie se base sur un couplage entre l’imagerie médicale et l’analyse par modélisation du comportement biomécanique du DIV. Un modèle physique biphasique du DIV est développé à l’aide de données déduites d’images IRM. Ce modèle tient compte de l’anisotropie du DIV et des effets induits par les déformations mécaniques sur le processus de transport nutritif.La partie mécanique du modèle est tout d’abord validée en utilisant des résultats d’essais de relaxation réalisés précédemment par l’équipe de biomécanique d’IRPHE. Le modèle est à même de reproduire la distribution de la porosité à l’intérieur du DIV et la perte d’eau suite à un effort mécanique. Cela permet de confirmer la fiabilité de la méthodologie de diagnostic développée pour les cas étudiés. En intégrant le couplage mécano-métabolique, la réponse biomécanique du DIV à des chargements quasi statiques et cycliques est ensuite analysée.Intervertebral disc degeneration (DD) is a natural process affecting a large part of the population. However, it can be accelerated by several factors and then become associated to lumbar disc pathologies. Given that the DD is symptomless, it is complicated to diagnostic it early. In this context, the present work aims to develop a new methodology of DD diagnostic based on medical imaging and biomechanical modeling.The intervertebral discs (IVD) are fibro-cartilaginous joints that connect vertebrae together ensuring their relative motion. They support and distribute loads applied on the spine. Mechanically, the IVD can be assimilated to a biological porous media in which the solid phase is composed of the extracellular matrix (MEC) and the fluid phase is formed by water content. It is a non-homogeneous and anisotropic component. The IVD is nonvascularized and characterized by a high water content in which cell nutrients diffuses from the adjacent vertebrae. Given this latter particularity, the porosity of the IVD presents a key factor in in its functioning especially when exposed to mechanical non-physiological loads. One of the main keys in the elaboration of a reliable DD diagnostic is to determine objectively some of the IVD interne properties related to its water content. In the present work, a new quantitative methodology of DD diagnostic is proposed. This method is based on a coupling between the medical imaging and the IVD biomechanical behavior modeling. A biphasic IVD model is developed using data from MRI imaging. This model takes into account the anisotropy of the IVD and the effect of the mechanical deformation on the nutrient transport process.The mechanical part of the developed model was firstly validated using results of relaxation tests previously performed by the biomechanics staff of IRPHE. The model is able to predict water distribution within the IVD and the water loss as response to a mechanical load. These results confirm the reliability of the developed diagnostic methodology for the studied cases. The biomechanical response to quasi-static and cyclic loads is then analyzed. The present work proposes and evaluates a new methodology of DD quantitative diagnostic. It also analyzes and compares mechanical and metabolic responses to quasi-static and cyclic loads

Modélisation du comportement biomécanique du disque intervertébral

Intervertebral disc degeneration (DD) is a natural process affecting a large part of the population. However, it can be accelerated by several factors and then become associated to lumbar disc pathologies. Given that the DD is symptomless, it is complicated to diagnostic it early. In this context, the present work aims to develop a new methodology of DD diagnostic based on medical imaging and biomechanical modeling.The intervertebral discs (IVD) are fibro-cartilaginous joints that connect vertebrae together ensuring their relative motion. They support and distribute loads applied on the spine. Mechanically, the IVD can be assimilated to a biological porous media in which the solid phase is composed of the extracellular matrix (MEC) and the fluid phase is formed by water content. It is a non-homogeneous and anisotropic component. The IVD is nonvascularized and characterized by a high water content in which cell nutrients diffuses from the adjacent vertebrae. Given this latter particularity, the porosity of the IVD presents a key factor in in its functioning especially when exposed to mechanical non-physiological loads. One of the main keys in the elaboration of a reliable DD diagnostic is to determine objectively some of the IVD interne properties related to its water content. In the present work, a new quantitative methodology of DD diagnostic is proposed. This method is based on a coupling between the medical imaging and the IVD biomechanical behavior modeling. A biphasic IVD model is developed using data from MRI imaging. This model takes into account the anisotropy of the IVD and the effect of the mechanical deformation on the nutrient transport process.The mechanical part of the developed model was firstly validated using results of relaxation tests previously performed by the biomechanics staff of IRPHE. The model is able to predict water distribution within the IVD and the water loss as response to a mechanical load. These results confirm the reliability of the developed diagnostic methodology for the studied cases. The biomechanical response to quasi-static and cyclic loads is then analyzed. The present work proposes and evaluates a new methodology of DD quantitative diagnostic. It also analyzes and compares mechanical and metabolic responses to quasi-static and cyclic loads.La dégénérescence des disques intervertébraux (DD) est un processus naturel qui touche une grande partie de la population. Toutefois, elle peut devenir pathologique et ainsi être accélérée par de multiples facteurs et conduire à un dysfonctionnement précoce des disques lombaires. La DD est un phénomène asymptomatique, ce qui le rend délicat à diagnostiquer précocement. Dans ce cadre, le présent travail vise à développer une méthodologie de diagnostic de la DD utilisant imagerie médicale et modélisation biomécanique.Les disques intervertébraux (DIV) sont des articulations fibro-cartilagineuses qui lient les vertèbres du rachis entre elles. Ils ont pour rôle de supporter et redistribuer les chargements appliqués au rachis tout en assurant sa mobilité. Le DIV peut être assimilé d’un point de vue mécanique à un milieu poreux biologique dont le squelette est formé par la matrice extracellulaire (MEC) et la phase fluide par son contenu hydrique. C’est un milieu hétérogène et anisotrope. Possédant une pression osmotique interne élevée, les DIV ne sont pas vascularisés et possèdent un contenu hydrique important dans lesquels les nutriments cellulaires diffusent à partir des vertèbres adjacentes. Cette particularité rend le fonctionnement des disques très dépendant de la régulation en eau en particulier lorsque ces derniers sont soumis à des sollicitations mécaniques non physiologiques.L’une des clés dans l’élaboration d’un diagnostic fiable de la DD peut être de déterminer de manière objective certaines des caractéristiques internes au disque relatives à son hydratation. Dans le présent travail, une méthodologie de diagnostic quantitatif de la viabilité discale et plus largement de la DD est ainsi proposée. Cette méthodologie se base sur un couplage entre l’imagerie médicale et l’analyse par modélisation du comportement biomécanique du DIV. Un modèle physique biphasique du DIV est développé à l’aide de données déduites d’images IRM. Ce modèle tient compte de l’anisotropie du DIV et des effets induits par les déformations mécaniques sur le processus de transport nutritif.La partie mécanique du modèle est tout d’abord validée en utilisant des résultats d’essais de relaxation réalisés précédemment par l’équipe de biomécanique d’IRPHE. Le modèle est à même de reproduire la distribution de la porosité à l’intérieur du DIV et la perte d’eau suite à un effort mécanique. Cela permet de confirmer la fiabilité de la méthodologie de diagnostic développée pour les cas étudiés. En intégrant le couplage mécano-métabolique, la réponse biomécanique du DIV à des chargements quasi statiques et cycliques est ensuite analysée

Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries’ healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19

Identification de systèmes par modèle non entier à partir de signaux d'entrée sortie bruités

Les principales contributions de cette thèse concernent l'identification à temps continu des systèmes par modèles non entiers dans un contexte à erreurs en les variables. Deux classes de méthodes sont développées : la première classe est fondée sur les statistiques d'ordre trois et la deuxième est fondée sur les statistiques d'ordre quatre. Dans chaque classe, deux cas différents sont distingués : le premier cas suppose que tous les ordres de dérivation non entiers sont connus a priori et seuls les coefficients de l'équation différentielle non entière sont estimés en utilisant les estimateurs fondés sur les statistiques d'ordre supérieur. Le deuxième cas suppose que les ordres de dérivation sont commensurables à un ordre nu estimé au même titre que les coefficients de l'équation différentielle non entière par des techniques d'optimisation non linéaire combinées aux estimateurs fondés sur les cumulants d'ordre trois et quatre. Des exemples de simulation numérique illustrent les développements théoriques. Des applications pratiques sur la modélisation du phénomène de diffusion de chaleur dans un barreau d'Aluminium et sur la modélisation d'un système électronique ont montré la pertinence des méthodes développées.This thesis deals with continuous-time system identification by fractional models in the EIV context. Two classes of methods are developed : the first class is based on third-order statistics and the second one is based on fourth-order statistics. Firstly, all differentiation orders are known a priori and only the coefficients of the differential equation are estimated using the developed algorithms based on higher-order statistics. Then, they are extended to estimate both the fractional differential equation coefficients and the commensurate order. Simulation examples display the theoretical developments on system identification in the EIV context. A practical application for modeling heat transfer phenomena in an aluminium rod and for modeling an electronic real system have shown the efficiency of the developed methods.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Intervertebral disc nutrition modeling : use of MRI to assess initial parameters

International audienc

Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19