This research was aimed at assessing comprehension, attitudes, and perspectives regarding artificial intelligence (AI) in dentistry. The null hypothesis was a lack of foundational understanding of AI in dentistry. This systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in May 2023. The eligibility criteria included cross-sectional studies published in English until July 2023, focusing solely on dentists or dental students. Data on AI knowledge, use, and perceptions were extracted and assessed for bias risk with the Joanna Briggs Institute checklist. Of 408 publications, 22 relevant articles were identified, and 13 studies were included in the review. The average basic AI knowledge score was 58.62 % among dental students and 71.75 % among dentists. More dental students (72.01 %) than dentists (62.60 %) believed in AI's potential for advancing dentistry. Thorough AI instruction in dental schools and continuing education programs for practitioners are urgently needed to maximize AI's potential benefits in dentistry. An integrated PhD program could drive revolutionary discoveries and improve patient care globally. Embracing AI with informed understanding and training will position dental professionals at the forefront of technological advancements in the field.

Dashti M, et al. Imaging Sci Dent. 2024 Sep;54(3):271-275. https://doi.org/10.5624/isd.20240037

With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed. The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs. Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17. Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias. The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.

Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. ML-based cephalometric imaging reduces errors, improves accuracy, and saves time. In this study, we conducted a meta-analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. Summary measurements included the mean departure from the 1–4-mm threshold or the percentage of landmarks identified within this threshold with a 95% confidence interval (CI). This meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. In conclusion, ML has shown promise for landmark detection in 2D cephalometric imagery, although the accuracy has varied among studies and clinicians. Consequently, more research is required to determine its effectiveness and reliability in clinical settings.

Objectives: The aim of this article is to establish a comprehensive nationwide prevalence of malocclusion traits on the sagittal, vertical, and transverse planes of space in the Turkish population.Material and Methods: A systematic search of PubMed, Scopus, and Web of Science was supplemented by manual searches of Google Scholar and the reference lists of included studies. Original Turkish health studies of any age were included. Strengthening the Reporting of Observational Studies in Epidemiology assessed study quality and bias (STROBE). Sagittal, vertical, and transverse malocclusion features were retrieved and gathered.Results: Eleven studies were selected from 434 titles. Two studies showed a high risk of bias, eight low and one moderate. Thirteen thousand two hundred seventy‐one individuals were investigated from early childhood to late adulthood. Most studies were sampled from universities and dental (nonorthodontic) clinics. The pooled malocclusion prevalence was 56% for Class I (95% confidence interval (CI): 44−68%), 31% for Class II (CI: 6–42%), and 11% for Class III (CI: 21–37%). The other common types of malocclusions were crowding (41%, CI: 18–65%), overjet (34%, CI: 21–50%), negative overjet (13%, CI: 7–20%), and crossbite (11%, CI: 7–15%). Additionally, there was no significant difference in Class I (relative risk [RR] = 1.00, [0.96–1.05]), Class II ([RR] = 0.97, [0.92–1.03]), and Class III ([RR] = 1.08, [0.96–1.225]) malocclusion by gender.Conclusions: This study showed Class I malocclusion has a high prevalence among the Turkish population followed by Class II and Class III malocclusions. In addition, crowding and overjet were the most prevalent malocclusions among Turkish individuals. There were no significant differences in the prevalence of malocclusions between males and females.