Home-isolation and social distancing during the COVID-19 pandemic caused increased use of digital devices, posing a greater risk of developing digital eye strain-related symptoms. Eye-care professi...

Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

There have been numerous and extensive studies into the visual requirements for reading in adults with low vision. There are far fewer studies involving children with low vision. This article compares the studies on children which do exist with the findings in adults. Acuity reserve (magnification), contrast reserve and visual field requirements are considered. We also review the literature which compares the efficacy of large print with optical magnification for children. From the few studies that exist, there are indications that the requirements for children are not the same as for adults. Therefore, we suggest that one cannot directly apply the results from adults to children and that there is a gap in the literature (and therefore our understanding) of the visual requirements for reading in children.

The W-D15 test, a computerized variant of the F-D15 test, is used to determine whether an individual with a color vision defect can safely perform color-related tasks. This study evaluated the performance of the W-D15 test using an iPad. Fifty-nine color normal and 61 color vision defect subjects participated. Participants were screened based on the Ishihara, City University Test, and Waggoner PIP24 tests. Different failure criteria of the D15 tests were considered. The pass/failure agreement between the two tests was relatively appropriate, with a κ -coefficient ≥ 0.8 , for all failure criteria. The W-D15 could be an appropriate substitute for F-D15 using an iPad.