Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies.

Background: In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies.

Salivary Biomarkers in Periodontitis: A Scoping Review.

Periodontal disease is a prevalent and potentially impactful oral health condition, ranging from gingivitis to severe periodontitis. Early detection and precise management are crucial in modern dentistry due to its prevalence and potential systemic health implications. Traditional clinical assessments and radiographic imaging have been the primary diagnostic tools.

Machine learning in the detection of dental cyst, tumor, and abscess lesions.

Background And Objective: Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.

CRISPR-Cas-Based Adaptive Immunity Mediates Phage Resistance in Periodontal Red Complex Pathogens.

Periodontal diseases are polymicrobial immune-inflammatory diseases that can severely destroy tooth-supporting structures. The critical bacteria responsible for this destruction include red complex bacteria such as , and . These organisms have developed adaptive immune mechanisms against bacteriophages/viruses, plasmids and transposons through clustered regularly interspaced short palindromic repeats (CRISPR) and their associated proteins (Cas).

Socio-behavioural determinants associated with the first dental visit in Saudi children: A cross-sectional study.

Backgroud: Oral health is an integral component of overall well-being, understanding the age at which children have their first dental visit (FDV) and the socio-behavioural factors influencing these visits is essential for improving oral health outcomes in children.

Aim: This study aimed to determine the age at which Saudi children had their FDV and the socio-behavioural predictors associated with these visits in Al Jouf Province, Kingdom of Saudi Arabia.

Design: This cross-sectional study used a multistage stratified random sampling technique to invite 566 parents/guardians of schoolchildren aged 12 years or younger.