Search

Your search keyword '"Dimitriou, S."' showing total 2 results
Start Over Author "Dimitriou, S." Publication Year Range Last 3 years
2 results on '"Dimitriou, S."'

1. Championing the use of people‐first language in childhood overweight and obesity to address weight bias and stigma: A joint statement from the European‐Childhood‐Obesity‐Group (ECOG), the European‐Coalition‐for‐People‐Living‐with‐Obesity (ECPO), the International‐Paediatric‐Association (IPA), Obesity‐Canada, the European‐Association‐for‐the‐Study‐of‐Obesity Childhood‐Obesity‐Task‐Force (EASO‐COTF), Obesity Action Coalition (OAC), The Obesity Society (TOS) and the World‐Obesity‐Federation (WOF)

Author

Weghuber, D., Khandpur, N., Boyland, E., Mazur, A., Frelut, M. L., Forslund, A., Vlachopapadopoulou, E., Erhardt, É., Vania, A., Molnar, D., Ring‐Dimitriou, S., Caroli, M., Mooney, V., Forhan, M., Ramos‐Salas, X., Pulungan, A., Holms, J. C., O'Malley, G., Baker, J. L., and Jastreboff, A. M.

Subjects
*BODY weight, *CHILDHOOD obesity, *DISCRIMINATION (Sociology), *PREJUDICES, *SOCIAL stigma, *ACADEMIC achievement, *QUALITY of life, *INTERPERSONAL relations, *DISEASE management, *PUBLIC opinion
Abstract

An editorial is presented on the importance of using people-first language in addressing weight bias and stigma in childhood overweight and obesity. Topics include the negative consequences of weight bias and stigma on children's health and well-being; and the adoption and enforcement of people-first language policies in academic research, healthcare settings, media, and public discourse surrounding childhood obesity.

Published
2023
Full Text
View/download PDF

2. Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests.

Author

Zignoli, A., Fornasiero, A., Rota, P., Muollo, V., Peyré-Tartaruga, L.A., Low, D.A., Fontana, F.Y., Besson, D., Pühringer, M., Ring-Dimitriou, S., and Mourot, L.

Subjects
*EXERCISE tests, *DEEP learning, *CARDIOPULMONARY system, *EXERCISE physiology, *ARTIFICIAL intelligence, *MACHINE learning, *CONCEPTUAL structures, *INTERPROFESSIONAL relations, *ANAEROBIC threshold, *ALGORITHMS
Abstract

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO2/min (11.1%, r = 0.97) and 144 (149) mlO2/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results