Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

Objectives: Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dental image segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, and unclear borders, resulting in poor image quality. Recent developments in deep learning models have improved performance in analyzing dental images. In this research, our primary objective is to determine the most effective segmentation technique for bitewing radiographs based on different metrics: accuracy, training time, and the number of training parameters as a reflection of architectural cost.

Prevalence of uncontrolled hypertension in primary care settings in Al seeb wilayat, oman.

Objectives: This study aimed to estimate the prevalence of uncontrolled hypertension (HTN) among Omani hypertensive patients, on treatment and under primary health care (PHC) follow-up in Al Seeb Wilayat, Oman. Socio-demographic and clinical factors were explored for possible influence on blood pressure (BP) control.

Methods: Based on an assumption of 50% prevalence of uncontrolled HTN, a retrospective data collection was conducted on the last three follow-up visits of 411 randomly selected Omani adults (≥18 years) from 3,459 hypertensive patients.