Age-related changes in reading performance in normally sighted Arabic-speaking adults.

CLINICAL RELEVANCE: Reading performance is important for evaluating near vision and predicting and prescribing near-vision correction. There is a significant gap in the literature on Arabic reading performance and its associated effects. BACKGROUND: Normative data from control participants serve as the baseline for future studies involving groups with specific characteristics. This study aimed to assess baseline measures of Arabic reading performance. METHODS: In total, 428 normally sighted adults aged 19-83 years read the Balsam Alabdulkader - Leat chart to measure their reading performance. The participants were divided into five age groups. The outcome measures were the maximum reading speed in standard-length words per minute, critical print size, reading acuity, and reading accessibility index. RESULTS: The average reading performance measures were as follows: 171 ± 27 standard-length words per minute for maximum reading speed, 0.01 logarithm of the minimum angle of resolution for critical print size, -0.17 logarithm of the minimum angle of resolution for reading acuity, and 1.02 for the accessibility index. There were significant differences among the age groups, indicating a dependency on age and showing a plateau for the young adult group and a decline with age. The accessibility index had an almost perfect correlation with maximum reading speed and a weak but significant relationship with reading acuity and critical print size. CONCLUSION: Arabic exhibits an age-dependent trend in reading speed. However, owing to differences in orthography, visual demands, and reading strategies, the magnitude of the decrease and increase differs, potentially accounting for the variations between languages, especially when compared to English. This study serves as a foundation for future studies on Arabic reading performance. Future studies should examine the reading performance in patients with low vision, study the effects of common diseases on reading ability, evaluate the effectiveness of reading devices, and assess improvements in vision rehabilitation.

Prevalence, Characteristics, and Management of Pediatric Ocular Trauma in Riyadh, Saudi Arabia: A Retrospective Analysis.

PURPOSE: Ocular trauma is a major cause of visual impairment; however, little is known about its burden in Saudi Arabia. Therefore, this study aimed to determine the epidemiological characteristics of ocular trauma in pediatric patients in Riyadh, Saudi Arabia. PATIENTS AND METHODS: Medical records of pediatric patients diagnosed with eye injuries between January 2016 and December 2020 were retrospectively reviewed. Demographic and injury characteristics were collected, and ocular trauma injuries were classified according to the Birmingham Eye Trauma Terminology. RESULTS: A total of 855 injured patients were included in the study, of whom 525 (61.4%) were boys. Patient age ranged from one month to 18 years. Most ocular injuries occurred in children aged 5-9 years. The injuries were more prevalent in boys than in girls. Closed globe injuries accounted for 70% of cases, open globe injuries for 21%, and other injuries for 9%. Most ocular injuries occurred at home (n = 87, 42%), followed by school (n = 61, 30%). CONCLUSIONS: These results may inform the implementation and targeting of interventions to reduce or prevent eye injuries in children. Further, they highlight the importance of well-planned prevention programs to prevent eye injuries from occurring in children's daily lives.

Visual Functions, Seatbelt Usage, Speed, and Alcohol Consumption Standards for Driving and Their Impact on Road Traffic Accidents.

Purpose:  The incidence of road traffic accidents (RTAs) is dramatically increasing worldwide. Consequently, driving and licensing authorities have instituted strict rules and regulations, such as vision standards, restrictions on drunk driving, seat belt usage, and speeding, for driving safety. This study aimed to summarize the global visual standards for driving license issuing and renewal and investigate the effect of driving safety laws on RTA-related death rates in different countries. Methods:  The study gathered data on visual standards for driving licenses from reliable sources and extracted enforcement scores (drunk driving, seat belt usage, and speeding) and RTA-related death rates from the World Health Organization status report on road safety. The Wilcoxon test explored the association between visual standards and RTA-related death rates, while the Kruskal-Wallis test analyzed the relationship between visual functions and death rates, as well as driving safety enforcement scores and RTA-related death rates. Results:  The analysis was conducted on 71 countries and 50 states within the United States out of the 193 countries listed by the United Nations. It was found that 116 countries and states required a minimum VA range of 6/6-6/18, while 91 countries and states mandated a similar range for one-eyed drivers. VF testing for driving licenses was necessary in 77 countries and states. No significant association was observed between VA or VF testing and RTA-related death rates. However, countries that conducted more visual function tests demonstrated lower rates of RTA-related fatalities. Furthermore, RTA-related death rates were significantly associated with speeding, drunk driving, and seat belt laws. Conclusion: Implementing clear policies regarding vision requirements, maintaining strict rules, and promoting law enforcement on speeding, drunk driving, and seat belt usage are crucial for improving road safety. These measures should be prioritized by driving and licensing authorities worldwide to mitigate the escalating incidence of RTAs.

Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images.

Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical decisions and improve healthcare quality. However, directly using US images can be challenging due to noise and diverse imaging modalities. In this study, we developed a three-step image processing scheme involving speckle noise filtering using a block-matching three-dimensional filtering technique, region of interest highlighting, and RGB fusion. This method enhances the generalization of deep-learning models and achieves better performance. We used a deep learning model (VGG19) to perform transfer learning on three datasets: BUSI (780 images), Dataset B (162 images), and KAIMRC (5693 images). When tested on the BUSI and KAIMRC datasets using a fivefold cross-validation mechanism, the model with the proposed preprocessing step performed better than without preprocessing for each dataset. The proposed image processing approach improves the performance of the breast cancer deep learning classification model. Multiple diverse datasets (private and public) were used to generalize the model for clinical application.

The Patients' Perspective for the Impact of Late Detection of Ocular Diseases on Quality of Life: A Cross-Sectional Study.

Background: Late detection of ocular diseases negatively affects patients' quality of life (QoL), encompassing health status, psychological, financial, and social aspects. However, the early detection of eye conditions leads to rapid intervention and avoiding complications, thus preserving the QoL. This study assessed the impact of ocular diseases late detection on patients' QoL at multi-eye clinics based on questionnaire responses. Methods: We developed an original Arabic-English questionnaire to assess the QoL of patients with ocular diseases referred from primary and secondary healthcare centers to tertiary hospitals. It covered preliminary data, patient perspectives on having lately detected ocular disease and treatment costs, and the impact of late detection on finances, social life, psychology, health status, and awareness of current initiatives. Logistic regression analysis was used to explore the associations between patient perspectives on having ocular diseases detected at a late stage and its impact on different domains. Multivariate logistic regression was applied with impact types of health status, psychological, financial, and social (dependent variables) and age, income levels, and hospital type (independent variables). Results: Three hundred and eighty-eight responded, with 50% experiencing psychological effects, 27% health issues, 23% social impacts, and 23% financial burdens. Two hundred seventeen patients (56%) reported having ocular condition detected in late stage. Logistic regression analysis showed positive association with health status, social well-being, and financial effects (p < 0.05). Multivariate analysis revealed pronounced effects in patients ≤ 50 years, with income \< 5000 SAR, and those visiting private clinics (p < 0.05). The social impact was greater in patients visiting private hospitals. Ninety percent of all patients emphasized the importance of increasing awareness for better QoL. Conclusion: Significant associations were found between the late detection of eye diseases and their impact on QoL. Therefore, early detection and increasing patients' awareness of ocular diseases and treatment are essential.

Peripapillary atrophy classification using CNN deep learning for glaucoma screening.

Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve's (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.