Objective: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. 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. Conclusions: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29^th 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. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

The present study was aimed at reviewing the studies that used finite element analysis (FEA) to estimate the biomechanical stress arising in removable partial dentures (RPDs) and how to optimize it. A literature survey was conducted for the English full‐text articles, which used only FEA to estimate the stress developed in RPDs from Jan 2000 to May 2021. In RPDs, the retaining and supporting structures are subjected to dynamic loads during insertion and removal of the prosthesis as well as during function. The majority of stresses in free‐end saddle (FES) RPDs are concentrated in the shoulder of the clasp, the horizontal curvature of the gingival approaching clasp, and the part of the major connector next to terminal abutments. Clasps fabricated from flexible materials were beneficial to eliminate the stress in the abutment, while rigid materials were preferred for major connectors to eliminate the displacement of the prosthesis. In implant‐assisted RPD, the implant receive the majority of the load, thereby reducing the stress on the abutment and reducing the displacement of the prosthesis. The amount of stress in the implant decreases with zero or minimal angulation, using long and wide implants, and when the implants are placed in the first molar area.

This systematic review aims to evaluate the antimicrobial activity of α-mangostin derived from Garcinia mangostana against different microbes. A literature search was performed using PubMed and Science Direct until March 2022. The research question was developed based on a PICO (Population, Intervention, Control and Outcomes) model. In this study, the population of interest was microbes, α-mangostin extracted from Garcinia mangostana was used as exposure while antibiotics were used as control, followed by the outcome which is determined by the antimicrobial activity of α-mangostin against studied microbes. Two reviewers independently performed the comprehensive literature search following the predetermined inclusion and exclusion criteria. A methodological quality assessment was carried out using a scoring protocol and the risk of bias in the studies was analyzed. Reward screening was performed among the selected articles to perform a meta-analysis based on the pre-determined criteria. Case groups where α-mangostin extracted from Garcinia mangostana was incorporated were compared to groups using different antibiotics or antiseptic agents (control) to evaluate their effectiveness. A total of 30 studies were included; they were heterogeneous in their study design and the risk of bias was moderate. The results showed a reduction in microbial counts after the incorporation of α-mangostin, which resulted in better disinfection and effectiveness against multiple microbes. Additionally, the meta-analysis result revealed no significant difference (p > 0.05) in their effectiveness when α-mangostin was compared to commercially available antibiotics. α-mangostin worked effectively against the tested microbes and was shown to have inhibitory effects on microbes with antibiotic resistance.