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Cet ouvrage propose de mieux comprendre les spécificités de la population étudiante en matière de santé, que ce soit en termes de pratiques ou de recours aux soins. Il rassemble les résultats de la première enquête nationale sur la santé des étudiants menée au cours de l’année universitaire 2015-2016, en parallèle de l’enquête Conditions de vie des étudiants 2016, auprès d’étudiants inscrits à l’université en France métropolitaine et outre-mer. Il s’inscrit également dans une démarche comparative avec les données disponibles sur la santé en population générale (Baromètre santé des Français et EuroBarometer).

Cet ouvrage propose de mieux comprendre les spécificités de la population étudiante en matière de santé, que ce soit en termes de pratiques ou de recours aux soins. Il rassemble les résultats de la première enquête nationale sur la santé des étudiants menée au cours de l’année universitaire 2015-2016, en parallèle de l’enquête Conditions de vie des étudiants 2016, auprès d’étudiants inscrits à l’université en France métropolitaine et outre-mer. Il s’inscrit également dans une démarche comparative avec les données disponibles sur la santé en population générale (Baromètre santé des Français et EuroBarometer).

Cet ouvrage propose de mieux comprendre les spécificités de la population étudiante en matière de santé, que ce soit en termes de pratiques ou de recours aux soins. Il rassemble les résultats de la première enquête nationale sur la santé des étudiants menée au cours de l’année universitaire 2015-2016, en parallèle de l’enquête Conditions de vie des étudiants 2016, auprès d’étudiants inscrits à l’université en France métropolitaine et outre-mer. Il s’inscrit également dans une démarche comparative avec les données disponibles sur la santé en population générale (Baromètre santé des Français et EuroBarometer).

Cet ouvrage propose de mieux comprendre les spécificités de la population étudiante en matière de santé, que ce soit en termes de pratiques ou de recours aux soins. Il rassemble les résultats de la première enquête nationale sur la santé des étudiants menée au cours de l’année universitaire 2015-2016, en parallèle de l’enquête Conditions de vie des étudiants 2016, auprès d’étudiants inscrits à l’université en France métropolitaine et outre-mer. Il s’inscrit également dans une démarche comparative avec les données disponibles sur la santé en population générale (Baromètre santé des Français et EuroBarometer).

Cet ouvrage propose de mieux comprendre les spécificités de la population étudiante en matière de santé, que ce soit en termes de pratiques ou de recours aux soins. Il rassemble les résultats de la première enquête nationale sur la santé des étudiants menée au cours de l’année universitaire 2015-2016, en parallèle de l’enquête Conditions de vie des étudiants 2016, auprès d’étudiants inscrits à l’université en France métropolitaine et outre-mer. Il s’inscrit également dans une démarche comparative avec les données disponibles sur la santé en population générale (Baromètre santé des Français et EuroBarometer).

Cet ouvrage souligne la diversité de ces expériences étudiantes, scolaires mais également extrascolaires, peut-être du fait d’une porosité de plus en plus grande entre conditions d’études et conditions de vie. Les différentes analyses présentées dans cet ouvrage soulignent la nécessité de convertir la diversité d’expériences en éléments de réussite individuelle et collective. Comme le suggèrent de nombreux résultats, le rôle des politiques étudiantes, locales, nationales et même internationales incluant les conditions d’études, la mobilité mais également l’accès à la santé, aux loisirs, au transport ou au logement est déterminant.

Cet ouvrage souligne la diversité de ces expériences étudiantes, scolaires mais également extrascolaires, peut-être du fait d’une porosité de plus en plus grande entre conditions d’études et conditions de vie. Les différentes analyses présentées dans cet ouvrage soulignent la nécessité de convertir la diversité d’expériences en éléments de réussite individuelle et collective. Comme le suggèrent de nombreux résultats, le rôle des politiques étudiantes, locales, nationales et même internationales incluant les conditions d’études, la mobilité mais également l’accès à la santé, aux loisirs, au transport ou au logement est déterminant.

Cet ouvrage souligne la diversité de ces expériences étudiantes, scolaires mais également extrascolaires, peut-être du fait d’une porosité de plus en plus grande entre conditions d’études et conditions de vie. Les différentes analyses présentées dans cet ouvrage soulignent la nécessité de convertir la diversité d’expériences en éléments de réussite individuelle et collective. Comme le suggèrent de nombreux résultats, le rôle des politiques étudiantes, locales, nationales et même internationales incluant les conditions d’études, la mobilité mais également l’accès à la santé, aux loisirs, au transport ou au logement est déterminant.

Cet ouvrage souligne la diversité de ces expériences étudiantes, scolaires mais également extrascolaires, peut-être du fait d’une porosité de plus en plus grande entre conditions d’études et conditions de vie. Les différentes analyses présentées dans cet ouvrage soulignent la nécessité de convertir la diversité d’expériences en éléments de réussite individuelle et collective. Comme le suggèrent de nombreux résultats, le rôle des politiques étudiantes, locales, nationales et même internationales incluant les conditions d’études, la mobilité mais également l’accès à la santé, aux loisirs, au transport ou au logement est déterminant.

Cet ouvrage souligne la diversité de ces expériences étudiantes, scolaires mais également extrascolaires, peut-être du fait d’une porosité de plus en plus grande entre conditions d’études et conditions de vie. Les différentes analyses présentées dans cet ouvrage soulignent la nécessité de convertir la diversité d’expériences en éléments de réussite individuelle et collective. Comme le suggèrent de nombreux résultats, le rôle des politiques étudiantes, locales, nationales et même internationales incluant les conditions d’études, la mobilité mais également l’accès à la santé, aux loisirs, au transport ou au logement est déterminant.

PurposeWe determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images.MethodsWe followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions.ResultsMean visual field mean deviation at baseline of the progressing glaucoma group was -7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit-intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm.ConclusionsBruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility.

PurposeWe determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images.MethodsWe followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions.ResultsMean visual field mean deviation at baseline of the progressing glaucoma group was -7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit-intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm.ConclusionsBruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility.

UnlabelledGlaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases.MethodThis paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class.ResultsThe proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively).ConclusionThe use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

UnlabelledGlaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases.MethodThis paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class.ResultsThe proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively).ConclusionThe use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the non-progressing and progressing glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.

Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.

Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.