Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate.

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.

Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review.

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.

The Short-Term Effect of Active Skeletonized Sutural Distractor Appliance on Temporomandibular Joint Morphology of Class III Malocclusion Subjects.

OBJECTIVES: The purpose of this study was to evaluate the short-term effect of active skeletonized sutural distractor (ASSD) appliance on temporomandibular joint morphology of class III malocclusion subjects. MATERIALS AND METHODS: This was a prospective interventional study. Cone-beam computerized tomography (CBCT) images of 22 patients were taken before and after treatment by using Planmeca Promax 3D CBCT machine version 2.9.2 (Planmeca OY Helsinki, Finland). The condylar width, height, length, roof of glenoid fossa thickness, and all joint spaces were measured. The condylar position was determined based on Pullinger and Hollander formula. The condylar shape was determined as per Kinzinger et al. The condylar volume was calculated by using Mimics software (Materialize, Belgium). STATISTICAL ANALYSIS: Data analysis was performed by using SPSS software version 24. Wilcoxon paired signed-rank test was used to compare the difference in temporomandibular joint morphology and condylar volume between pre- and post-treatment measurements. Chi-square test was used to compare the condylar position and shape. RESULTS: The superior (p = 0.000 on the right side, p = 0.005 on the left side) and posterior joint spaces (p = 0.000 on both sides) were decreased after the treatment, respectively. The condyles were rotated upward and backward, thereby increasing the anterior joint spaces (p = 0.000 on both sides) after the treatment. The condylar volume increases after treatment, but no significant differences were observed (p = 0.903 on the right side, p = 0.062 on the left side). CONCLUSION: The significant changes were observed in joint spaces. The condyles were more anteriorly placed before treatment. Condylar position and shape alter in response to ASSD treatment. The condylar volume did not show any significant change.