Needle-Stick and Sharp Injuries among Hospital Healthcare Workers in Saudi Arabia: A Cross-Sectional Survey

Needle-stick or sharp injuries (NSIs) are critical occupational hazards for healthcare workers. Exposure to blood and body fluids through NSIs increases the risk of transmission of blood-borne pathogens among them. The objectives of this study were to estimate the annual incidence of NSIs and investigate the associated factors of NSIs among the healthcare workers in Saudi Arabia. A cross-sectional online survey was conducted between October and November 2021. A total of 361 healthcare workers participated in the survey from all over Saudi Arabia. The one-year incidence of at least one event of NSIs among the healthcare workers is estimated at 22.2% (95% CI: 18.0, 26.8). More than half of the injury events (53.8%) were not reported to the authority by the healthcare workers. Incidence of NSIs was highest among the physicians (36%) and was followed by nurses (34.8%), dentists (29.2%), and medical technologists (21.1%). The odds of NSIs was higher among the healthcare workers aged 26–30 years compared to the 20–25 years age group (OR: 2.51; 95% CI: 1.04, 6.03), as well as among the workers who directly dealt with needles or other sharp objects while working compared to those who did not (OR: 5.9; 95% CI: 2.69, 12.97). The high incidence and low rate of reporting of NSIs highlights the need of education and awareness raising programs targeting healthcare providers with higher risk of injury

Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review

Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities

Criterion Validity of the Newly Developed Occlusal Cant Index

Objectives: To assess the criterion-related (concurrent) validity of the newly developed occlusal cant index (OCI). Materials and Methods: Four standardized posterior–anterior (PA) cephalometric radiographs of four patients were obtained at a 0° occlusal cant (OC) and manipulated to create various degrees of OC from 1° to 4° on the right and left sides, with a total of 36 PA images. The angle between the actual horizontal line and the occlusal plane was manually drawn on each PA radiographic image. The set of radiographic images was displayed to 36 orthodontists, who were asked to measure the drawn angle and apply the OCI to each PA radiographic image. Results: The overall criterion-related validity of the OCI was statistically significant among all grades. Conclusion: The OCI is highly valid and recommended for clinical consideration

Cephalometric Soft Tissue Characteristics of Unilateral Cleft Lip and Palate Patients in Relation to Missing Teeth

Objective. This study aimed to evaluate cephalometric soft tissue characteristics in individuals with unilateral complete cleft lip and palate (UCCLP) both with and without missing teeth. Design. A retrospective investigation of patient records, who are being treated at the cleft lip and palate (CLP) clinics at the College of Dentistry. Ninety-six consecutive records of nonsyndromic UCCLP subjects were recruited (33 subjects without missing teeth and 63 subjects with missing teeth). Linear and angular soft tissue measurements obtained from lateral cephalometric radiographs were evaluated and compared among the studied samples. Results. Lower lip was significantly retruded and shorter (p=0.037), p=0.015, respectively; in addition to the fact that shallower mentolabial sulcus (p=0.05) was found in the subjects with missing teeth, the rest of the soft tissue was not significantly different between the two groups. Conclusion. In subjects with a UCCLP anomaly, missing teeth have an effect on lower lip position and length, which influenced the mentolabial sulcus. Lower lip position and length differ between cleft patients who present with either multiple missing teeth or with no missing teeth, and this needs to be considered during orthodontic treatment planning and surgical management for the cleft defect