Computer vision syndrome (CVS) is an umbrella term for a pattern of symptoms associated with prolonged digital screen exposure, such as eyestrain, headaches, blurred vision, and dry eyes. Commercially available blue light filtering lenses (BLFL) are advertised as improving CVS. Our pilot study evaluates the effectiveness of BLFL on reducing CVS symptoms and fatigue in a cohort of radiologists. A prospective crossover study was conducted with ten radiology residents randomized into two cohorts: one wearing BLFL first then a sham pair (non-BLFL), and the other wearing a sham pair first then BLFL, over two weeks during normal clinical work. Participants filled out a questionnaire using the validated computer vision syndrome questionnaire (CVS-Q) and the Swedish Occupational Fatigue Inventory (SOFI). The majority of symptoms [11/16 (68.8%) and 13/16 (81.3%) symptoms on the CVS-Q and SOFI, respectively] were reduced (i.e., symptoms less severe) with the BLFL compared to the sham glasses. Females rated symptoms of sleepiness and physical discomfort in the SOFI, and overall CVS-Q, as more severe. Postgraduate year (PGY)-2 residents rated all symptoms as more severe than PGY-3/4s. BLFL may ameliorate CVS symptoms. Future studies with larger sample sizes and participants of different ages are required to verify the potential of BLFL.
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http://dx.doi.org/10.1117/1.JMI.7.2.022402 | DOI Listing |
Vis Intell
December 2024
Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
View Article and Find Full Text PDFJ Rehabil Assist Technol Eng
January 2025
University of Regina, Regina, SK, Canada.
Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings.
View Article and Find Full Text PDFDIS (Des Interact Syst Conf)
June 2022
Pennsylvania State University, University Park, PA, USA.
Remote sighted assistance (RSA) has emerged as a conversational assistive service, where remote sighted workers, ., agents, provide real-time assistance to blind users via video-chat-like communication. Prior work identified several challenges for the agents to provide navigational assistance to users and proposed computer vision-mediated RSA service to address those challenges.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
Introduction: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. There is a paucity of evidence on the impact of AI algorithms in assisting healthcare professionals (other than radiologists) who regularly review CXR images in their daily practice.
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