Aim: This study aimed to assess the prevalence of Computer Vision Syndrome (CVS) among children and adolescents in Qatar during the period of remote learning and explore the associated factors and discuss some digital health remedies that might reduce the risk.
Methods: We conducted an analytical cross-sectional study between June and August 2022 by collecting data via telephone interviews with parents of selected students utilizing the Computer Vision Syndrome Questionnaire (CVS-Q).
Results: We completed 1,546 interviews. The mean age of the students was (11 ± 2), male: female ratio was almost 1:1. About one quarter (368, 23.8%) of parents reported a previous diagnosis of visual disturbances among their children with over 88% of them wearing eyeglasses or medical contact lenses. The prevalence of CVS in our sample was about 8% (95%CI: 6.8-9.6). Mother's employment, having positive history of visual disturbances, and excess screen time were found to be significant predictors of CVS.
Conclusion: Health care providers in collaboration with teachers should provide parents with evidence-based strategies to prevent or minimize the digital eye strain among students. In the landscape of remote learning, the implementation of digital remedies emerges as a proactive approach to mitigate the risk of digital eye strain.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666185 | PMC |
http://dx.doi.org/10.3389/fpubh.2023.1273886 | DOI Listing |
JMIR Res Protoc
January 2025
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Background: Individuals with hearing impairments may face hindrances in health care assistance, which may significantly impact the prognosis and the incidence of complications and iatrogenic events. Therefore, the development of automatic communication systems to assist the interaction between this population and health care workers is paramount.
Objective: This study aims to systematically review the evidence on communication systems using human-computer interaction techniques developed for deaf people who communicate through sign language that are already in use or proposed for use in health care contexts and have been tested with human users or videos of human users.
Transl Vis Sci Technol
January 2025
School of Optometry and Vision Science, University of New South Wales, Sydney, Australia.
Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.
Proc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Nigdi, Pune 411044, India.
Recent advancements in artificial intelligence (AI) have increased interest in intelligent transportation systems, particularly autonomous vehicles. Safe navigation in traffic-heavy environments requires accurate road scene segmentation, yet traditional computer vision methods struggle with complex scenarios. This study emphasizes the role of deep learning in improving semantic segmentation using datasets like the Indian Driving Dataset (IDD), which presents unique challenges in chaotic road conditions.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!