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.

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.

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