HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors.

Study Objectives: This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection.

Methods: HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for the fundus data, in conjunction with a fully connected neural network for age and sex.

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.

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.

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.

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.

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.

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.