Introduction: Computer vision syndrome (CVS) has become a significant issue for individuals working on computers and digital devices for extended periods. The ocular and periocular symptoms and signs associated with CVS are a major concern, affecting individuals physically and financially. Additionally, CVS has been linked to the rapid progression of myopia, exacerbating the situation. Blinking has been one of the major treatment methods for the treatment of CVS. This study presents a unique and novel randomized controlled therapeutic trial that evaluates the impact of extended blinking therapy on eye health and vision, along with other related parameters. Materials and methods: The present study is a randomized controlled trial conducted from September 2022 to April 2024. Participants aged 18-40 with CVS and a computer vision syndrome questionnaire (CVS-Q) score of ≥6, with mild to moderate refractive error (between -6D and +4D), were included. The sample size was determined based on a pilot study, resulting in a minimum required sample size of 36 patients (18 cases and 18 controls). Participants were randomly assigned to either the case (interventional) or control (conventional) group and were followed up for six months. Cases received conventional CVS treatment plus optimized blinking exercises, while controls received conventional therapy only. Comprehensive ocular assessments were conducted bi-monthly over six months, evaluating changes in uncorrected visual acuity (UCVA), refractive error, near point of accommodation (NPA), near point of convergence (NPC), Schirmer's test, and tear film breakup time (TBUT).
Results: The study included 20 patients in the case group and 18 in the control group, primarily aged 20-29 (60.5%). Most patients used laptops for their activities (55.26%). The CVS-Q score significantly decreased in both groups following treatment, with both cases and controls showing significant improvement (p<0.001 for both groups). UCVA in the right eye (RE) and left eye (LE) of the cases improved significantly post-treatment in the interventional group (RE: p=0.002; LE: p<0.001). A significant change in refractive error, which is measured as spherical equivalent (SE), was seen among cases following treatment (RE: p<0.001; LE: p=0.021). Controls showed no significant changes in visual acuity or refractive error. The NPA in the cases improved significantly in the RE (p=0.027) but not in the left. The NPC in the intervention group showed no significant change, while controls showed considerable improvement (p=0.042). Schirmer's test results showed no significant change in either group. However, TBUT in the cases improved significantly (RE: p<0.001; LE: p<0.001). In the controls, TBUT decreased significantly, indicating a deterioration in tear film stability. Asthenopia grades improved considerably in cases, while controls showed only some improvement. Severe symptoms still remained in the control group, emphasizing the potential benefits of the blinking exercise in reducing asthenopia symptoms.
Conclusion: Optimized blinking therapy significantly improves vision and refractive error, tear film stability, and discomfort, making it beneficial for chronic computer users to maintain ocular health and enhance productivity and quality of life.
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http://dx.doi.org/10.7759/cureus.67653 | DOI Listing |
Eur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
Neuroinformatics
January 2025
Department of Clinical Medicine, UiT the Arctic University of Norway, Tromsø, Norway.
Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal.
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January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
iScience
January 2025
Division of Optometry, Health Sciences, City University of London, London EC1V 0HB, UK.
A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.
The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.
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