Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

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 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. 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

Combination of skin flap and silicone prosthesis for rehabilitation of a large orbital defect: A case report

Exenteration surgery greatly affects a person in terms of function, esthetics, and psychological trauma. In such cases, restoration by silicone orbital prosthesis is a well-accepted treatment option. However, this is a difficult task, necessitating personalized design of method for each patient. This case report describes the technique for fabrication of a silicone orbital prosthesis for a male patient with left orbital defect due to exenteration of a Grade 3 squamous cell carcinoma of the left eye and surrounding tissues. The patient was delivered with a satisfactory silicone orbital prosthesis having good retention and finish. Multidisciplinary management and team approach are crucial in providing precise and effective rehabilitation for improving the patient's quality of life and help them return to their normal social life

The Influence of Filler Particles on the Mechanical Properties of Maxillofacial Prosthetic Silicone Elastomers: A Systematic Review and Meta-Analysis

Although numerous studies have demonstrated the benefits of incorporating filler particles into maxillofacial silicone elastomer (MFPSE), a review of the types, concentrations and effectiveness of the particles themselves was lacking. The purpose of this systematic review and meta-analysis was to review the effect of different types of filler particles on the mechanical properties of MFPSE. The properties in question were (1) tensile strength, (2) tear strength, (3) hardness, and (4) elongation at break. The findings of this study can assist operators, technicians and clinicians in making relevant decisions regarding which type of fillers to incorporate based on their needs. The systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 original articles from 1970 to 2019 were selected from the databases, based on predefined eligibility criteria by two reviewers. The meta-analyses of nine papers were carried out by extracting data from the systematic review based on scoring criteria and processed using Cochrane Review Manager 5.3. Overall, there were significant differences favoring filler particles when incorporated into MFPSE. Nano fillers (69.23% of all studies) demonstrated superior comparative outcomes for tensile strength (P < 0.0001), tear strength (P < 0.00001), hardness (P < 0.00001) and elongation at break (P < 0.00001) when compared to micro fillers (30.76% of all studies). Micro fillers demonstrated inconsistent outcomes in mechanical properties, and meta-analysis of elongation at break argued against (P < 0.01) their use. Current findings suggest that 1.5% ZrSiO4, 3% SiO2, 1.5% Y2O3, 2–6% TiO2, 2–2.5% ZnO, 2–2.5% CeO2, 0.5% TiSiO4 and 1% Ag-Zn Zeolite can be used to reinforce MFPSE, and help the materials better withstand mechanical degradation

A 2D Photographic and 3D Digital Dental Model Analysis of Golden Percentage in Maxillary Anterior Teeth

The objective of this paper was to evaluate the existence of golden percentage in natural maxillary anterior teeth with the aid of 3D digital dental models and 2D photographs. And to propose regional values of golden percentage for restoration of maxillary anterior teeth. For this purpose, one hundred and ninety dentate subjects with sound maxillary anterior teeth were selected. Standardized frontal images were captured with DSLR, and the apparent width of maxillary anterior teeth was measured utilizing a software on a personal laptop computer. Once the dimensions were recorded, the calculations were made according to the golden percentage theory (GPT). The data were analyzed by independent and paired T-test. The level of significance was set at p<0.05. The golden percentage values were not found in this study. The values obtained were 16%, 15%, 20%, 20%, 15%, and 16% moving from the right canine to the left canine teeth. There was no significant gender difference in the golden percentage values. Thus, golden percentage should not be used solely for the correction of anterior teeth or for determining dental attractiveness. Emphasis should be given to a range of dental proportion on regional basis

Effects of Riboflavin Collagen Crosslinker on Dentin Adhesive Bonding Efficiency: A Systematic Review and Meta-Analysis

The aim of this study was to evaluate published data regarding riboflavin (RF) as a cross-linker for improved adhesive bond strength to dentin and to analyze previous studies for optimal concentration of riboflavin range suitable for dentin bond. Saliva and distilled water were used as storage media and aging time was 24 h and 6 months. Results of meta-analysis were synthesized using a statistical method of inverse variance in random effects with a 95% Confidence Interval (CI). Cochrane review manager 5.4.1 was used to determine results of the meta-analysis. In total, 3172 articles were found from search databases “PubMed”, “Scopus”, and “Google Scholar”. Six of the fifteen studies were eligible for meta-analysis. Micro tensile strength shows significant improvement with the addition of riboflavin (p 2 for micro tensile strength was 89% with strong heterogeneity, Chi2 = 44.76, df = 5 (p p = 0.03) after immediate aging. Chiang et al. 2013 shows maximum mean differences which is 38.50 [17.93–59.07]. After 6 months of aging in distilled water or artificial saliva micro tensile bond strength has been increased with the addition of riboflavin (p 2 for micro tensile strength was 96% with strong heterogeneity, Chi2 = 117.56, df = 5 (p p = 0.02). Subgroup analysis proved a similar effect of distilled water and artificial saliva as storage media on micro tensile bond strength after incorporating riboflavin as a collagen crosslinker. An artificial saliva aged forest plot also showed considerable heterogeneity with I2 = 96%; Tau2 = 257.32; Chi2 = 94.37; df = 2 (p p = 0.29). Riboflavin prior to or with bonding is recommended to improve the bonding of different adhesive systems

The Potential of &alpha;-Mangostin from Garcinia mangostana as an Effective Antimicrobial Agent&mdash;A Systematic Review and Meta-Analysis

This systematic review aims to evaluate the antimicrobial activity of &alpha;-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, &alpha;-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 &alpha;-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 &alpha;-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 &alpha;-mangostin, which resulted in better disinfection and effectiveness against multiple microbes. Additionally, the meta-analysis result revealed no significant difference (p &gt; 0.05) in their effectiveness when &alpha;-mangostin was compared to commercially available antibiotics. &alpha;-mangostin worked effectively against the tested microbes and was shown to have inhibitory effects on microbes with antibiotic resistance