Effectiveness of a School-Based Oral Health Promotion Program on Dental Caries Among Iraqi School Children: A Cluster Randomised Controlled Trial.

Aim: To assess the effectiveness of a school-based oral health promotion program on dental caries of permanent dentition among Iraqi children.

Methods: A cluster randomised controlled trial was conducted with a parallel study group, comprising 8-10-year-old schoolchildren, 186 in each group. At the beginning of the study, subjects in the intervention group received oral health education and a single dose of 5% sodium fluoride varnish for all teeth surfaces while the control group only received oral health education.

The association between dental caries, periodontal diseases, and tooth loss with diabetes mellitus among the elderly population.

Purpose: Our study investigated the association between dental caries, periodontal diseases, and tooth loss with diabetes mellitus (DM) among the elderly population, representing the first study of its kind in Iran.

Methods: This was a cross-sectional community-based study as a part of the second wave of the Birjand longitudinal aging study (BLAS, 2021-2022) for people older than 60. We used the Comprehensive Geriatric Oral Health Assessment Tool (CGOHAT) for the oral health evaluation.

Association between dental clinical measures and oral health-related quality of life among Iraqi schoolchildren: A cross-sectional study.

Objective: The aim of this study was to assess the association between dental clinical measures and oral health-related quality of life (OHRQoL) together with the potential mediating role of sociodemographic factors and oral health behaviours on this association in a group of Iraqi children.

Methods: The target population for this cross-sectional study consisted of 372 primary school children aged 8-10 years in the city of Kut, Iraq, during the year 2022. The participants were selected using a multi-stage random sampling technique.

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method.

Breast cancer's high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks-EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50-integrated via our innovative consensus-adaptive weighting (CAW) method.