Purpose: To translate, cross-culturally adapt and validate the Computer Vision Syndrome Questionnaire (CVS-Q) into Persian.
Methods: This study was carried out in 2 phases: (1) the CVS-Q was translated and cross-culturally adapted into Persian and (2) the validity and reliability of CVS-Q FA were assessed in a cross-sectional validation study. An expert committee composed of 15 optometrists evaluated content validity (item-level (I-CVI) and scale-level (S-CVI) content validity index were calculated). A pretest was performed (n = 20 participants) to verify the comprehensibility of the questionnaire. A total of 102 computer users completed the final questionnaire. Criterion validity and diagnostic performance of the CVS-Q FA were assessed by calculating sensitivity, specificity and receiver characteristic operator curve. Cronbach's alpha was calculated for the assessment of internal consistency and 46 participants refilled the questionnaire for the second time and the interclass correlation coefficient (ICC) and Cohen's kappa (κ) were evaluated for test-retest reliability.
Results: The translation and cross-cultural adaptation process was performed successfully according to accepted scientific recommendations without any major difficulties. The I-CVI was above 0.80 for all items (symptoms) except item 15 (feeling that sight is worsening) and the S-CVI was 0.92. The CVS-Q FA showed good sensitivity (81.1%) and acceptable specificity (69.2%). Also, it achieved good internal consistency (Cronbach's alpha = 0.80) and test-retest reliability (ICC = 0.81 and κ = 0.65).
Conclusion: The CVS-Q FA was successfully translated, cross-culturally adapted, and validated into Persian. This study provides a valid and reliable tool for the assessment of computer vision syndrome among the Iranian working population.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9092937 | PMC |
http://dx.doi.org/10.1007/s10792-022-02340-3 | DOI Listing |
Sci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
View Article and Find Full Text PDFBioData Min
January 2025
School of Computer Science, Fudan University, Shanghai, China.
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
View Article and Find Full Text PDFNeural Netw
December 2024
Institute of Automation, Chinese Academy of Sciences, MAIS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explores alternatives to the standard convolution, with the objective of augmenting its feature extraction prowess while maintaining a similar parameter count. We propose innovative solutions targeting depthwise separable convolution and standard convolution, culminating in our Multi-scale Progressive Inference Convolution (MPIC).
View Article and Find Full Text PDFPLoS One
January 2025
Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
Alzheimers Dement
December 2024
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Background: The human brain is a complex inter-wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system-level understanding of how brain anatomy supports various neural activities remains elusive.
Method: Capitalizing on the unprecedented amount of neuroimaging data, we present a physics-informed deep model to uncover the coupling mechanism between brain structure and function through the lens of data geometry that is rooted in the widespread wiring topology of connections between distant brain regions.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!