Evaluating the anterior chamber angle with gonioscopy is an essential part of evaluating patients at risk of contracting glaucoma and neovascularization. It also provides useful information for patients with iris abnormalities or a history of trauma or ocular inflammation. Unfortunately, gonioscopy is underutilized by both optometrists and ophthalmologists. There are many symptoms that call for performing gonioscopy. One of the most common reasons for performing gonioscopy is in cases where the doctor suspects a patient is at risk of being infected with angle closure glaucoma (ACG) with pupil dilation. This paper aims at calculating the angle of open and closed glaucoma as a first step of determining the thickness of the liquid available on the cornea, which has been proven to be very helpful in preventing vision loss. The techniques involved in this study include proposing an eye segmentation process, the calculation of the angle opening distance (AOD), trabecular iris angle (TIA), and trabecular iris space area (TISA). The simulation results obtained and the performance of the proposed method are found to be highly efficient and accurate.
Sustainable Learning and Education (SLE) is a recent emerging philosophy founded on sustainability principles and in response to the UN announced Sustainable Development Goals (SDGs). Therefore, technologies should be implemented to empower educational institutions to achieve SLE. This study aims to investigate the factors impacting the intentions of using blockchain technology for SLE in Jordanian universities. Accordingly, an extended Technology Acceptance Model (TAM) is proposed where five more factors are integrated. To this end, an extended model was proposed and validated using structural equation modeling based on 407 responses collected using an online survey. The results showed that adopted factors significantly impact blockchain use in SLE. We believe that the study finding would assist decision-makers in building systems for sustainable learning and education for the Jordanian higher educational institutes.
Intraocular pressure (IOP) in general refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions/symptoms that may lead to certain diseases such as glaucoma and therefore must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. A new smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images. The framework monitors the status of IOP risk by analyzing frontal eye images using image processing and machine learning techniques. A database of images collected from Princess Basma Hospital in Jordan was used in this work. The database contains 400 eye images: 200 images with normal IOP and 200 high eye pressure case images. The framework extracts five features from the frontal eye image: the pupil and iris diameter ratio, mean redness level of the sclera, red area percentage of the sclera, and two other features measured from the extracted contour of the sclera (contour height and contour area). Once the features were extracted, a neural network is trained and tested to obtain the status of the patients in terms of eye pressure. The framework detects the status of IOP (normal or high IOP) and produces evidence of the relationship between the five extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques using frontal eye images.
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