Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: 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. PURPOSE: 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. MATERIAL AND METHODS: 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. RESULTS: 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. CONCLUSIONS: The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.

Evaluation of the golden proportion in the natural dentition: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: Different proportions of tooth width ratios have been evaluated, and the golden proportion is the best known. Although the prevalence of the golden proportion has been evaluated in different ethnic groups and populations, a meta-analysis of these studies is lacking. PURPOSE: The purpose of this systematic review and meta-analysis was to investigate the existence and suitability of the golden proportion for tooth width ratios and to assess the prevalence of the golden proportion among populations. MATERIAL AND METHODS: A systematic search based on the Patient, Intervention, Comparison, Outcome (PICO) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in PubMed, Cochrane, Scopus, Web of Science, and Embase in January 2021 without any time limitations and included English and non-English-language articles. Additional studies were identified by searching the reference lists of the retrieved articles. Two reviewers independently performed the literature search and data extraction, selecting articles based on the inclusion and exclusion criteria. Eligible studies were selected based on the inclusion criteria, and quality assessments were conducted. Descriptive statistics were applied for a number of outcome measures. Using a meta-analysis software program, data extracted from each selected study were statistically combined by using weighted mean differences and 95% confidence intervals, and heterogeneity was calculated for each measurement. RESULTS: A total of 566 articles were retrieved based on the keyword search. After the articles were assessed based on the inclusion and exclusion criteria, only 6 articles were included in the meta-analysis. CONCLUSIONS: The outcomes of the review and analyses demonstrate that evidence supporting the existence of the golden proportion in natural smiles is lacking and that the existence of the golden proportion in dentistry is a myth and not a fact. However, the golden percentage theory can be applied with modified percentages that consider critical aspects of esthetic dentistry.

How much can we rely on artificial intelligence chatbots such as the ChatGPT software program to assist with scientific writing?

STATEMENT OF PROBLEM: Use of the ChatGPT software program by authors raises many questions, primarily regarding egregious issues such as plagiarism. Nevertheless, little is known about the extent to which artificial intelligence (AI) models can produce high-quality research publications and advance and shape the direction of a research topic. PURPOSE: The purpose of this study was to determine how well the ChatGPT software program, a writing tool powered by AI, could respond to questions about scientific or research writing and generate accurate references with academic examples. MATERIAL AND METHODS: Questions were made for the ChatGPT software program to locate an abstract containing a particular keyword in the Journal of Prosthetic Dentistry (JPD). Then, whether the resulting articles existed or were published was determined. Questions were made for the algorithm 5 times to locate 5 JPD articles containing 2 specific keywords, bringing the total number of articles to 25. The process was repeated twice, each time with a different set of keywords, and the ChatGPT software program provided a total of 75 articles. The search was conducted at various times between April 1 and 4, 2023. Finally, 2 authors independently searched the JPD website and Google Scholar to determine whether the articles provided by the ChatGPT software program existed. RESULTS: When the author tested the ChatGPT software program's ability to locate articles in the JPD and Google Scholar using a set of keywords, the results did not match the papers that the ChatGPT software program had generated with the help of the AI tool. Consequently, all 75 articles provided by the ChatGPT software program were not accurately located in the JPD or Google Scholar databases and had to be added manually to ensure the accuracy of the relevant references. CONCLUSIONS: Researchers and academic scholars must be cautious when using the ChatGPT software program because AI-generated content cannot provide or analyze the same information as an author or researcher. In addition, the results indicated that writing credit or references to such content or references in prestigious academic journals is not yet appropriate. At this time, scientific writing is only valid when performed manually by researchers.

Assessment of the golden proportion in natural facial esthetics: A systematic review.

STATEMENT OF PROBLEM: Faces considered attractive tend to possess a general harmony; however, scientific studies on the quantitative basis of human facial attractiveness are still sparse. The golden proportion has been the most documented ratio, but the actual existence of codified facial ratios in facially attractive individuals remains unclear. PURPOSE: The purpose of this systematic review was to evaluate the deviation from the golden proportion in the measurements of different natural vertical facial ratios. MATERIAL AND METHODS: A comprehensive search of the electronic databases that included PubMed/MEDLINE, Web of Science, Embase, Scopus, Scopus Secondary documents, and the Cochrane Database of Systematic Reviews was conducted using the terms "golden proportion", "golden proportions", "golden ratio", "golden ratios", "golden number", "golden section", "divine proportion", "divine proportions", "divine ratio", "divine ratios", and "Fibonacci" in combination with 1 or more of the ensuing terms: "facial proportion", "facial proportions", "facial measurement", and "facial measurements". Deviation of the facial proportions from the golden proportions was further analyzed by using a 1-sample t test (α=.05). RESULTS: The 1-sample test showed no significant difference among the means of each variable (P>.05), suggesting that the golden proportion is found in natural facial esthetics. CONCLUSIONS: No significant association was found between the golden ratio and facial evaluation scores among all ethnicities. The measurements and proportions for facial balance in the study population showed that the participant's facial height proportions did not follow the golden proportion.

Attitudes, knowledge, and perceptions of dentists and dental students toward artificial intelligence: a systematic review.

Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.

Clear aligner therapy versus conventional brackets: Oral impacts over time.

Background: The objective is to compare the impact of clear aligner treatment (CAT) versus conventional fixed appliance treatment (FAT) on oral health-related quality of life (OHRQoL) among adults at five-time points: pretreatment (T0), 1 week (T1), 1 month after (T2), and 6 months after (T3) treatment initiation, and in the long-term follow-up (T4). Materials and Methods: Search terms were based on Medical Subject Headings (MeSH) and non-MeSH. Potentially eligible studies compared OHRQoL in clear aligner (CA) and fixed appliance (FA) patients. In February 2023, Scopus, Web of Science, Cochrane, and PubMed were searched for published studies. Nine out of 94 shortlisted papers were eligible for a systematic review. Of these nine papers, five studies were considered for a meta-analysis. Results: At T0, CA and FA patients had similar oral health impact profile (OHIP)-14 questionnaire scores with a standard mean difference (SMD) of 0.105 (confidence interval [CI]: -1.029-1.48). The SMD of the OHRQoL related to T1, T2, and T3 was -3.119 (CI: -0.145, 0.355), -1.527 (CI: -5.597, -0.64), and - 2.331 (CI: -1.906, -1.148). T4 showed no difference between groups (SMD = 0.007, CI: CI: -4.286, -0.376). Regarding the OHIP-14 domains, functional limitations remained consistent in both groups across all time intervals. Psychological discomfort exhibited a notable difference only at T2. Throughout the treatment, CAT showed significantly lower levels of physical, psychological, and social disability, as well as handicap, though these differences did not persist beyond T4. Notably, physical pain was the sole domain that remained elevated in the FAT group up to T4. Conclusion: During the 1st day of the orthodontic treatment, both the CA and FA groups had comparable OHRQoL statuses. However, as time passed, the CA group notably improved their OHRQoL compared to the FA group. Interestingly, after a year or the completion of treatment, both groups eventually reached similar OHRQoL levels. Nevertheless, it is worth noting that FA patients continued to experience more physical pain even a year later.

Onset of Mucormycosis in Patients with COVID-19: A Systematic Review on Patients' Characteristics.

Mucormycosis has a significant impact on patients' standard of living and, therefore, a high clinical suspicion, prediagnosis, and rapid treatment are critical in easing patients' suffering and fast recovery. Our focus is to conduct an organized review based on various variables on the patients' characteristics having mucormycosis in severe novel coronavirus disease 2019 (COVID-19). We examined Embase, PubMed-Medline, LitCovid, Web of Science, Scopus, and the reference lists of included case reports up to September 20, 2021, using the Medical Subject Heading (MeSH) phrases and other keywords related to this topic. Subsequently, we investigated associated comorbidities, patient characteristics, position of mucormycosis, steroids use, body involvements, and outcomes. Overall, 77 studies were conducted and among these, 72 studies mentioned that the patients' age to be 48.13±14.33 (mean±standard deviation [SD]) years. Diabetes mellitus (DM) was reported in 77.9% (n=60) of cases. Studies showed that central nervous system (CNS) and bone involvement were reported in 62.3 (n=48) and 53.2% (n=41), respectively. More fatalities were observed in patients with mucormycosis with the active form of COVID-19. Also, men infected with mucormycosis significantly affected by COVID-19. In the end, mortality was higher in males with mucormycosis. As a result, a solid investigation into the root cause of mucormycosis, especially in COVID-19, should be included in the study plan. If the patient is COVID-19-positive and immunosuppressed, this opportunistic pathogen diagnostic test should not be overlooked.

Prevalence of malocclusion in Turkish children and adolescents: A systematic review and meta-analysis.

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.

Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis.

Introduction: 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. Method: 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. Result: 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. Conclusion: 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.

Fight against COVID-19 with mRNA vaccines and interaction with Dermal fillers.

The current coronavirus disease 2019 (COVID-19) outbreak has created a huge demand for rapid and high-volume vaccine manufacturing. Several new platform technologies and traditional manufacturing approaches are being used to meet this demand. Among them, the mRNA vaccine platform technology holds great promise for obtaining an emergency use authorization to facilitate immunization against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which causes the respiratory illness COVID-19. However, this is a new technology, which means that there is significant uncertainty about possible production at a large scale and speed. Vaccines of mRNA work by providing our cells with the genetic code to make viral proteins. Once proteins that do not cause any disease are produced, the body triggers an immune response against the virus, allowing them to develop immunity. mRNA can be used to make any protein theoretically. Still, it is much easier to produce than the proteins themselves or the inactivated and attenuated versions of viruses commonly used in vaccines, which makes this technique attractive, says mRNA specialist Norbert Pardi, at the University of Pennsylvania.