Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study.

Background: Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI].

Corrigendum to "A study on the association between the inferior nasal turbinate volume and the maxillary sinus mucosal lining using cone beam tomography" [Heliyon Volume 8, Issue 3, MARCH 2022, Article e09190].

[This corrects the article DOI: 10.1016/j.heliyon.

Advanced Computational Methods for Radiation Dose Optimization in CT.

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable".

Evolving radiographic practice: Identifying possible skill requirements for future radiographers practicing in the United Arab Emirates (UAE).

Aim: This study aims to identify the prospective skill requirements for future radiographers practising in the United Arab Emirates (UAE). Such information will inform educational institutions, healthcare organisations, and policymakers in developing effective strategies.

Methods: A cross-sectional study was conducted involving currently practising radiographers, nuclear medicine technologists, sonographers, and radiation therapists in the UAE (n =74).

CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images.

Introduction: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images.

Methods: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images.

Embedding patient safety in a scaffold of interprofessional education; a qualitative study with thematic analysis.

Background: Regardless of a proliferation of interest in reducing unsafe practices in healthcare, threats to patient safety (PS) remain high. Moreover, little attention has been paid towards the role of interprofessional education (IPE) in enhancing PS. This qualitative study was conducted to unfold the insights of the senior medical, dental and health sciences students at the University of Sharjah (UoS) in the United Arab Emirates (UAE) about PS in an online IPE-based workshop.

Systematic Review on Diagnostic Reference Levels for Computed Tomography Examinations in Radiation Therapy Planning.

: In August 2017, the European Commission awarded the "European Study on Clinical Diagnostic Reference Levels (DRL) for X-ray Medical Imaging" project to the European Society of Radiology to provide up-to-date Diagnostic Reference Levels based on clinical indications. This work aimed to conduct an extensive literature review by analyzing the most recent studies published and the data provided by the National Competent Authorities to understand the current situation regarding Diagnostic Reference Levels based on clinical indications for Radiation Therapy Computed Tomography. : To review the literature on established DRLs and methodologies for establishing Diagnostic reference levels in radiation therapy planning computed tomography (RTCT).

Community and Policy Factors Influencing the Decision to Undergo Screening Mammography amongst Indian Women in the United Arab Emirates.

Objective: This study explored the expectations, requirements, and recommendations and identifies the socio-ecological determinants for the informed uptake of screening mammography amongst expatriate women residing in the UAE.

Methodology: A qualitative research approach was adopted using five focus group discussions. The study comprised two populations- those who had undergone screening mammography and those who had not.

A study on the association between the inferior nasal turbinate volume and the maxillary sinus mucosal lining using cone beam tomography.

Objectives: The volume of the inferior turbinates (IT) and the lining of the maxillary sinuses are important parameters when assessing sino-nasal diseases. However, no radiographic studies have investigated the correlation between these parameters. The present study was conducted to analyze the association between IT volumes and thickness of maxillary sinus mucosal lining.

The impact of an online intervention on the medical, dental and health sciences students about interprofessional education; a quasi-experimental study.

Background: Interprofessional education (IPE) encompasses integration, communication, mutual trust and shared decision-making with a common goal of improved patient care and safety. Despite its crucial role, IPE has not gained its anticipated popularity. This study aims to determine the impact of an online educational intervention about IPE on medical, dental and health sciences students in the University of Sharjah (UoS).

Acceptability and potential impacts of innovative E-Portfolios implemented in E-Learning systems for clinical training.

Objective: There is growing evidence that use of E-portfolios has a positive effect on learning experience. However, literature has not shown sufficient data about the effectiveness of E-portfolios in medical education. This study was conducted to assess the acceptability and potential impacts of E-portfolio use for undergraduate radiography students.