Prevalence and Determinants of Symptomatic Dry Eye Disease Among Adult Urban Residents of High-Altitude Areas of Southwest Saudi Arabia - A Survey.

Purpose: To estimate the prevalence and determinants of Ocular Surface Disease Index (OSDI) score based dry eye disease (DED) among the adult urban population of four cities located at high altitudes in Southwest Saudi Arabia. Methods: This cross-sectional survey was held in 2023. OSDI questionnaire was used to collect the responses of the adult participants. The score was further graded into none, mild, moderate, and severe DED to estimate age-sex-adjusted DED prevalence. The OSDI score was correlated to demographic (age group, gender, education, occupation, city) and risk factors like smoking and co-morbidities. Results: Of the 401 adults, 388 (response rate of 97.8%) participated. The age-sex-adjusted prevalence of mild, moderate, and severe DED was 21.7%, 13.1%, and 32%, respectively. The median ODSI score was 22.9 [Interquartile range (IQR) 10.4; 47.9)]. The score was significantly higher in females (Mann-Whitney U-test P = 0.038), residents of Taif city (KW P = 0.05), those with primary/middle school education (Kruskal-Wallis P = 0.004), comorbidities like hypertension, asthma (KW P < 0.001) and risk factors like past refractive surgeries, arthritis (KW P = 0.013). Education status (P <0.001) [B = -9.0 95%] and presence of comorbidity (P = 0.022), [B = -0.823] were significant predictors of DED. Conclusion: The prevalence of DED and severe grade was high. The level of education and presence of comorbidities significantly influenced DED in the adult urban Saudi population of cities at high altitudes.

Medical Schools' Ophthalmology Course: An Appraisal by Ophthalmology Residents.

OBJECTIVE: To investigate the perception and satisfaction of ophthalmology residents with the currently provided ophthalmology curricula to medical students. METHODS: A cross-sectional survey involving first to fourth year ophthalmology residents (N = 106) from all regions of Saudi Arabia was conducted between December 2018 and February 2019. An online questionnaire explored opinions about the ophthalmology course regarding three dimensions. Firstly, adequacy in covering essential parts of the specialty; secondly, improvements required; and thirdly, effectiveness. A score (0-21) was calculated, indicating the overall suitability of the ophthalmology course. In addition, factors of good overall suitability (score ≥10) were analyzed. RESULTS: Regarding adequacy, respondents opined that the ophthalmology course did not reasonably cover the basic part (35.8%), clinical part (61.3%), common disease (26.4%), and emergencies (39.6%). Concerning improvements required, more than 80% of the participants expressed that the course required to be improved for all its features, including duration (80.2%), objectives (85.8%), content (82.1%), organization (83.0%), and supervision (81.1%). As to effectiveness, half of them deemed the course unhelpful in familiarizing general practitioners with common ophthalmic diseases and emergencies. Overall, the ophthalmology course was generally deemed suitable (score ≥10) for only 27.4% of the participants, with no differences across gender, level, or region. CONCLUSION: Ophthalmology residents perceived multiple deficits in the current Saudi ophthalmology teaching course. Significant improvements in ophthalmologic curricula are required, besides coping with unprecedented technological advancement in the ophthalmological field.

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.

The prevalence and the clinical characteristics of metabolic syndrome patients admitted to the cardiac care unit.

OBJECTIVES: Metabolic syndrome (MetS) is a group of multiple cardiovascular risk factors, including dysglycemia, central obesity, high cholesterol, and hypertension. Cardiovascular disease is one of the most common complications of MetS. Recent studies showed that prevalence of MetS among patients admitted with acute coronary syndrome was as high as 46%. DESIGN: We conducted a cross-sectional study of 203 patients at the two main hospitals in Ta'if, Saudi Arabia. Patients older than 18 years who were admitted to the Cardiac Care Unit (CCU) between the months of August 2013 and June 2014 were asked to participate. MetS diagnosis was made based on the International Diabetes Federation definition. RESULTS: A total of 203 patients participated, with 59.1% male and 40.9% were female. The mean age was 60.9 years with a mean body mass index of 28.97 kg/m(2) and a mean waist circumference of 95.45 cm. The prevalence of MetS was 47.8%, primarily among obese female patients who reported sedentary lifestyles. Additionally, MetS patients were more likely to be admitted with heart failure (p < 0.05) and more likely to have moderate-to-severe left-ventricular hypertrophy (LVH; p < 0.05) relative to non-MetS patients. CONCLUSION: Of the patients admitted to the CCU, 47.8% had MetS, with those patients likely to be female and obese. Furthermore, MetS patients were more likely to be admitted with heart failure and suffer from moderate-to-severe LVH.