Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.

Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.

Results: 87 articles were screened to get an update on the desired information. 74 were excluded based on a complete screening, and finally, 13 articles were recruited for complete reviewing. Discussion. The MFP is subjected to stress, which is reflected in the form of compressive and tensile strengths. The stress is mainly concentrated the resection line and around the apices of roots of teeth next to the defect. Diversity of designs and techniques were introduced to optimize the stress distribution, such as modification of the clasp design, using materials with different mechanical properties for dentures base and retainer, use of dental (DI) and/or zygomatic implants (ZI), and free flap reconstruction before prosthetic rehabilitation.

MATERIALS AND METHODS: Two reviewers independently searched two electronic databases, PubMed and Scopus. The search was complemented from references of included studies and published reviews. Studies published in the English language through January 2021 that had assessed and documented the clinical and radiographic failure of crown or FPD in vital permanent teeth due to pulpal or periapical pathology with a follow-up of at least 12 months were selected. Data screening, data collection and extraction of data was performed. Quality of studies involved was analyzed using the Newcastle-Ottawa Quality Assessment Scale for cohort studies. Meta-analysis was done using random effects model. Publication bias was assessed using funnel plots.

RESULTS: Electronic searches provided 10,075 records among which 20 studies were selected for systematic review and 7 studies were selected for meta-analysis. With respect to quality assessment, all the studies involved were considered as high quality as the score in scale ranged between 6 and 9 as per the Newcastle-Ottawa Quality Assessment Scale for cohort studies. The meta-analyses showed that there was no statistically significant difference in the incidence of the loss of pulp vitality or pulp necrosis through clinical and radiographic examination with the follow up period of 5 years: p < 0.001, 95% CI: 0.96-1.00, I2 = 77.84%; 10 years: p < 0.001, 95% CI: 0.88-0.95, I2 = 93.59%; 15 years: p < 0.001, 95% CI: 0.92-0.96, I2 = 94.83%; and 20 years: p < 0.001, 95% CI: 0.94-0.96, I2 = 95.01%.

MATERIALS AND METHODS: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.

RESULTS: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.

PURPOSE: The purpose of this virtual analysis study was to compare the accuracy and precision of 3-dimensional (3D) ear models generated by scanning gypsum casts with a smartphone camera and a desktop laser scanner.

MATERIAL AND METHODS: Six ear casts were fabricated from green dental gypsum and scanned with a laser scanner. The resultant 3D models were exported as standard tessellation language (STL) files. A stereophotogrammetry system was fabricated by using a motorized turntable and an automated microcontroller photograph capturing interface. A total of 48 images were captured from 2 angles on the arc (20 degrees and 40 degrees from the base of the turntable) with an image overlap of 15 degrees, controlled by a stepper motor. Ear 1 was placed on the turntable and captured 5 times with smartphone 1 and tested for precision. Then, ears 1 to 6 were scanned once with a laser scanner and with smartphones 1 and 2. The images were converted into 3D casts and compared for accuracy against their laser scanned counterparts for surface area, volume, interpoint mismatches, and spatial overlap. Acceptability thresholds were set at <0.5 mm for interpoint mismatches and >0.70 for spatial overlap.

RESULTS: The test for smartphone precision in comparison with that of the laser scanner showed a difference in surface area of 774.22 ±295.27 mm2 (6.9% less area) and in volume of 4228.60 ±2276.89 mm3 (13.4% more volume). Both acceptability thresholds were also met. The test for accuracy among smartphones 1, 2, and the laser scanner showed no statistically significant differences (P>.05) in all 4 parameters among the groups while also meeting both acceptability thresholds.

MATERIALS AND METHODS: The literature search was carried out on two electronic databases (PubMed and Cochrane Library). Randomized controlled trials (RCT) published from January 2011 to September 2022 were included. The bias risk was evaluated using Cochrane Risk of Bias Tool 2.0. Further screening was done for meta-analysis according to modified Newcastle-Ottawa scoring criteria. Forest plot was generated using a statistical method of inverse variance of random effect with 95% confidence interval.

RESULTS: A total of 8 randomized controlled trials were included for systematic review out of which four studies were based on tooth-supported fixed prosthesis and remaining four were based on implant-supported prosthesis. Further screening was conducted and three studies were eligible for meta-analysis. Tooth-supported fixed prosthesis fabricated from digital impression showed no significant difference in the marginal fit in any region measured, except for occlusal region where conventional impression showed more favorable marginal fit. Implant-supported prosthesis fabricated from digital impression showed survival rates ranging from 97.3 to 100% and there was no statistically significant difference in marginal bone loss (p = 0.14).

METHODS: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.

RESULTS: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.