Background The purpose of this investigation was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the accuracy and usefulness of this system for the detection of alveolar bone loss in periapical radiographs in the anterior region of the dental arches. We also aimed to evaluate the usefulness of the system in categorizing the severity of bone loss due to periodontal disease. Method A data set of 1724 intraoral periapical images of upper and lower anterior teeth in 1610 adult patients were retrieved from the ROMEXIS software management system at King Saud bin Abdulaziz University for Health Sciences. Using a combination of pre-trained deep CNN architecture and a self-trained network, the radiographic images were used to determine the optimal CNN algorithm. The diagnostic and predictive accuracy, precision, confusion matrix, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen Kappa, were calculated using the deep CNN algorithm in Python. Results The periapical radiograph dataset was divided randomly into 70% training, 20% validation, and 10% testing datasets. With the deep learning algorithm, the diagnostic accuracy for classifying normal versus disease was 73.0%, and 59% for the classification of the levels of severity of the bone loss. The Model showed a significant difference in the confusion matrix, accuracy, precision, recall, f1-score, MCC and Matthews Correlation Coefficient (MCC), Cohen Kappa, and receiver operating characteristic (ROC), between both the binary and multi-classification models. Conclusion This study revealed that the deep CNN algorithm (VGG-16) was useful to detect alveolar bone loss in periapical radiographs, and has a satisfactory ability to detect the severity of bone loss in teeth. The results suggest that machines can perform better based on the level classification and the captured characteristics of the image diagnosis. With additional optimization of the periodontal dataset, it is expected that a computer-aided detection system can become an effective and efficient procedure for aiding in the detection and staging of periodontal disease.

The objective of this paper was to evaluate the studies that have reported on psychological issues among dental students in Saudi Arabia and to develop coping strategies to overcome these mental health-related issues. The present systematic review is in accordance with the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search for the articles was carried out in the electronic databases by four independent researchers. The data search was performed in the electronic search engines like PubMed, Google Scholar, Web of Science, Scopus, Medline, Embase, Cochrane and Saudi Digital Library for scientific research articles published from January 2000 until December 2020. STROBE guidelines were adopted for qualitative analysis of six articles which met the eligibility criteria. The analysis of the literature revealed that most of the studies included were conducted in the past 8 years in different regions of Saudi Arabia. Findings of this systematic review clearly state that dental students in Saudi Arabia experience higher levels of depression, stress and anxiety and stress during their education period, with a higher stress for female students compared to male students. There is an urgent need to introduce interventional programs and preventive strategies to overcome the long-term effects.

Aim. This study is aimed at combining the sample sizes of all studies on permanent maxillary teeth conducted in different regions of the Kingdom of Saudi Arabia (KSA) to obtain a large sample size that represents the population of the KSA. The outcome of these combined studies is compared with international studies in terms of the number of roots, number of canals, and canal configurations on the basis of Vertucci’s classification. Methodology. The studies were systematically reviewed using the Preferred Reporting Items for Systematic Review and Meta-analysis chart. Studies were included in the analysis if they were conducted in the KSA, involved permanent human maxillary teeth, and had a sample of more than 10 teeth (power). By contrast, studies were excluded if they involved deciduous teeth in the sample size, investigated nonhuman teeth, were not conducted in the KSA, and were case reports, case series, review studies, and anomalies. Relevant literature was searched from PubMed, Scopus, Web of Science, Embase, Cochrane, and Direct Science by two calibrated teams, starting in August 2020, without time limits or language restrictions. Results. The database searches and cross-referencing identified a total of 19 relevant studies. All maxillary canines ( N = 1,018 ) had one root, whereas 98.4% had one canal and 98.3% had Vertucci type I. Moreover, 63.2% of the maxillary first premolars had two roots, and 91.4% had two canals. The most common Vertucci root canal configuration was type IV (64.6%). The maxillary second premolars mostly had one root (84.4%) and one canal (50.4%). The most common canal configuration was Vertucci type I (47.1%). The majority of maxillary first molars had three roots (98.9%), 48.7% of which had three canals, and 46.4% had four canals. The most prevalent feature of the canal morphology of mesiobuccal roots was Vertucci type II (35.3%). The investigated maxillary second molars had three roots, 88.0% of which had three canals. Conclusion. This systematic review represents the Saudi population since samples were combined from different studies from different regions of the country. Variations in findings were observed in the same group of teeth from different regions and the same region, while the overall combined samples results fell within the range of other international studies.

Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.

Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.

Oral diseases are the most prevalent chronic childhood diseases, presenting as a major public health issue affecting children of all ages in the developing and developed countries. Early detection and control of these diseases is very crucial for a child’s oral health and general wellbeing. The aim of this systematic review is to assess the performance of artificial intelligence models designed for application in pediatric dentistry. A systematic search of the literature was conducted using different electronic databases, primarily (PubMed, Scopus, Web of Science, Embase, Cochrane) and secondarily (Google Scholar and the Saudi Digital Library) for studies published from 1 January 2000, until 20 July 2022, related to the research topic. The quality of the twenty articles that satisfied the eligibility criteria were critically analyzed based on the QUADAS-2 guidelines. Artificial intelligence models have been utilized for the detection of plaque on primary teeth, prediction of children’s oral health status (OHS) and treatment needs (TN); detection, classification and prediction of dental caries; detection and categorization of fissure sealants; determination of the chronological age; determination of the impact of oral health on adolescent’s quality of life; automated detection and charting of teeth; and automated detection and classification of mesiodens and supernumerary teeth in primary or mixed dentition. Artificial intelligence has been widely applied in pediatric dentistry in order to help less-experienced clinicians in making more accurate diagnoses. These models are very efficient in identifying and categorizing children into various risk groups at the individual and community levels. They also aid in developing preventive strategies, including designing oral hygiene practices and adopting healthy eating habits for individuals.

Background and objectives: Investigation into the impact of dental trauma on the results of orthodontic treatment is crucial because it can have a major influence on patient care. However, there has not yet been a thorough review or meta-analysis of the available data, which is inconsistent and scant. Therefore, the goal of this systematic review and meta-analysis is to investigate the impact of dental trauma on orthodontic parameters. Search methods and criterion of selection: Major online databases were searched (beginning from the year 2011) for relevant articles using a properly defined search strategy. Analysis protocol: Risk of bias (RoB) and the Cochrane risk of bias tool were utilized for the purposes of bias evaluation within the individual studies and within the review, respectively. Results: Out of the six clinical trials selected, a significant impact of trauma was observed in individuals in all but one paper. Gender predilection varied across studies and could not be conclusively determined. The follow-up period ranged from two months to two years in the trials. The odds ratio (OR) 0.38 [0.19, 0.77] and the risk ratio (RR) 0.52 [0.32, 0.85] indicated that both the odds as well as the relative risk of experiencing dental trauma were lower in the group with negligible impact compared to the group with noticeable impact. Conclusion and further implications: The findings show that dental trauma significantly affects orthodontic parameters, with lower risk and likelihood of suffering dental trauma in the group with negligible impact than in the group with noticeable impact. However, given the substantial heterogeneity among the studies, it is advised to exercise caution when extrapolating the findings to all populations. Registration and protocol: Registration in the PROSPERO database was carried out before initiating the investigation [CRD42023407218].

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