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.

Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. 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%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.

AIM. The objective of this study is to understand the knowledge of periodontal medicine among third year, final year and dental interns. MATERIALS AND METHODS. This cross-sectional questionnaire-based study was conducted among dental students in Chengalpattu district, Tamil Nadu – India, belonging to third year, final year and Interns. A total of 15 questions about the prevalence, etiology, signs, association, risk factors, and treatment of periodontal medicine were included in the questionnaire. RESULTS. Based on our study findings, a Moderate level of overall periodontal medicine knowledge was found. Although there were differences between genders in years of study in regards to disease prevalence / reasons / associations – there were statistically significant differences (p < 0.05) regarding the clinical signs of periodontal disease (i.e., clinical signs of scurvy). A few associations were also identified as having a relationship between periodontal disease as well as two or more systemic diseases i.e., atherosclerotic heart disease. In each domain of knowledge there were identifiable risk factors associated with periodontal disease – while low-dose doxycycline was found by essentially all participants to be the best treatment of periodontitis for diabetic patients in comparison to other medications. There was no statistically significant difference found for either gender or the year of study. CONCLUSIONS. Among the dental students, there is a good amount of knowledge regarding the relationship between periodontal disease and various systemic illnesses. Further, knowledge should be spread on anticipatory guidance to patients on periodontal disease and its association with systemic illnesses.

Orofacial clefts (OFCs) are one of the most prevalent congenital abnormalities that affect the lip and/or palate and can cause significant growth retardati