Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Fernández-Álvarez, J.; Rozental, A.; Carlbring, P.; Colombo, D.; Riva, G.; Anderson, P. L.; Baños, R. M.; Benbow, A. A.; Bouchard, S.; Bretón-López, J. M.; Cárdenas, G.; Difede, J.; Emmelkamp, P.; García-Palacios, A.; Guillén, V.; Hoffman, H.; Kampann, I.; Moldovan, R.; Mühlberger, A.; North, M.; Pauli, P.; Castro, W. Peñate; Quero, S.; Tortella-Feliu, M.; Wyka, K.; Botella, C.
Deterioration rates in Virtual Reality Therapy: An individual patient data level meta-analysis Article de journal
Dans: Journal of Anxiety Disorders, vol. 61, p. 3–17, 2019, ISSN: 08876185 (ISSN), (Publisher: Elsevier Ltd).
Résumé | Liens | BibTeX | Étiquettes: Adolescent, adult, aged, Anxiety disorder, Anxiety Disorders, article, demography, deterioration, Deterioration rates, disease association, female, human, Humans, Individual patient data analysis, male, marriage, meta analysis, Middle Aged, Odds Ratio, outcome assessment, Probability, psychology, randomized controlled trial (topic), social status, virtual reality, virtual reality exposure therapy, Young Adult
@article{fernandez-alvarez_deterioration_2019,
title = {Deterioration rates in Virtual Reality Therapy: An individual patient data level meta-analysis},
author = {J. Fernández-Álvarez and A. Rozental and P. Carlbring and D. Colombo and G. Riva and P. L. Anderson and R. M. Baños and A. A. Benbow and S. Bouchard and J. M. Bretón-López and G. Cárdenas and J. Difede and P. Emmelkamp and A. García-Palacios and V. Guillén and H. Hoffman and I. Kampann and R. Moldovan and A. Mühlberger and M. North and P. Pauli and W. Peñate Castro and S. Quero and M. Tortella-Feliu and K. Wyka and C. Botella},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050381788&doi=10.1016%2fj.janxdis.2018.06.005&partnerID=40&md5=012fdcaa9b8676314cbf22c52c91135b},
doi = {10.1016/j.janxdis.2018.06.005},
issn = {08876185 (ISSN)},
year = {2019},
date = {2019-01-01},
journal = {Journal of Anxiety Disorders},
volume = {61},
pages = {3–17},
abstract = {Ample evidence supports the use of Virtual Reality (VR) for anxiety disorders. Nonetheless, currently there is no evidence about moderators or potential negative effects of VR treatment strategies. An Individual Patient Data (IPD) approach was employed with 15 retrieved datasets. The current study sample was composed of 810 patients. Randomized control trials (RCTs) for each primary outcome measure were performed, in addition to moderator analyses of the socio-demographic variables. Deterioration rates were 14 patients (4.0%) in VR, 8 (2.8%) in active control conditions, and 27 (15%) in the WL condition. With regard to receiving treatment, patients in a waiting list control condition had greater odds of deteriorating than in the two active conditions, odds ratios (ORs) 4.87, 95% confidence interval (CI) [0.05, 0.67]. In the case of the socio-demographic variables, none of them were associated with higher or lower odds of deterioration, with the exception of marital status in the WL condition; married people presented a significantly lower probability of deterioration, OR 0.19, 95% CI [0.05, 0.67]. Finally, when comparing pooled effects of VR versus all control conditions, the OR was 0.61 (95% CI 0.31–1.23) in favor of VR, although this result was not statistically significant. This study provides evidence about the deterioration rates of a therapeutic VR approach, showing that the number of deteriorated patients coincides with other therapeutic approaches, and that deterioration is less likely to occur, compared to patients in WL control groups. © 2018 Elsevier Ltd},
note = {Publisher: Elsevier Ltd},
keywords = {Adolescent, adult, aged, Anxiety disorder, Anxiety Disorders, article, demography, deterioration, Deterioration rates, disease association, female, human, Humans, Individual patient data analysis, male, marriage, meta analysis, Middle Aged, Odds Ratio, outcome assessment, Probability, psychology, randomized controlled trial (topic), social status, virtual reality, virtual reality exposure therapy, Young Adult},
pubstate = {published},
tppubtype = {article}
}
Ample evidence supports the use of Virtual Reality (VR) for anxiety disorders. Nonetheless, currently there is no evidence about moderators or potential negative effects of VR treatment strategies. An Individual Patient Data (IPD) approach was employed with 15 retrieved datasets. The current study sample was composed of 810 patients. Randomized control trials (RCTs) for each primary outcome measure were performed, in addition to moderator analyses of the socio-demographic variables. Deterioration rates were 14 patients (4.0%) in VR, 8 (2.8%) in active control conditions, and 27 (15%) in the WL condition. With regard to receiving treatment, patients in a waiting list control condition had greater odds of deteriorating than in the two active conditions, odds ratios (ORs) 4.87, 95% confidence interval (CI) [0.05, 0.67]. In the case of the socio-demographic variables, none of them were associated with higher or lower odds of deterioration, with the exception of marital status in the WL condition; married people presented a significantly lower probability of deterioration, OR 0.19, 95% CI [0.05, 0.67]. Finally, when comparing pooled effects of VR versus all control conditions, the OR was 0.61 (95% CI 0.31–1.23) in favor of VR, although this result was not statistically significant. This study provides evidence about the deterioration rates of a therapeutic VR approach, showing that the number of deteriorated patients coincides with other therapeutic approaches, and that deterioration is less likely to occur, compared to patients in WL control groups. © 2018 Elsevier Ltd
2.
Allili, M. S.; Ziou, D.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Article de journal
Dans: Geomatica, vol. 59, no 3, p. 275–281, 2005, ISSN: 11951036.
Résumé | Liens | BibTeX | Étiquettes: Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping
@article{allili_automatic_2005-2,
title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},
issn = {11951036},
year = {2005},
date = {2005-01-01},
journal = {Geomatica},
volume = {59},
number = {3},
pages = {275–281},
abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},
keywords = {Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping},
pubstate = {published},
tppubtype = {article}
}
In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.