Background: Cataract is defined as the loss of lens transparency because of opacification of the lens. Agerelated cataract is the most prevalent type in adults, with the onset between age 45 to 50 years, while in children hereditary and metabolic causes are most common. Aim of the work: In this study, our aim was to discuss the epidemiology, pathophysiology, classification, risk factors, symptoms, and management of cataracts. Methodology: We conducted this review using a comprehensive search of MEDLINE, PubMed and EMBASE from January 1981 to March 2017. The following search terms were used: cataracts, causes of cataract, pathophysiology of cataract, management of cataract. Conclusion: Cataract is one of the most common causes of blindness worldwide, and more prevalent in developing countries. It is also the most curable cause of blindness which involves a simple surgical procedure. Keywords: cataracts, causes of cataract, pathophysiology of cataract, management of cataract.

The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography. Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed. Results: In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%-97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations. Conclusions: The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset. Translational Relevance: This article takes research on machine vision and evaluates its readiness for clinical use.

Background: The headache is one of the most common neurological disorders and ranks the third cause of years lost due to disability. So this study was conducted to identify the prevalence of headache and its impact on job performance in emergency department medical and paramedical staff.

Background In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. Methods The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines. Results External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. Conclusions We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

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