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The purpose of this study was to describe the morphology and measure the size of the sella turcica in Iraqi population and compared with available global data. Computed Tomography (CT) images of 71 individuals (49 males and 22 females) with an age range of 33.9 years were taken. Conventional measurements included three different heights of the sella turcica (anterior, posterior, median), its length, diameter and width, measured in relation to the Frankfort reference line (FH). In addition, the area of sella turcica was calculated. Morphometric methods were used to assess shape. Multiple statistical analyses were done to calculate differences in dimensions and to establish if any relationship exists between age, sex and the morphometry of the sella turcica. No significant differences in size of the sella were found between genders. When age was evaluated, all dimensions showed positive correlation with the age. Sella size of the older age group was as a rule larger than the younger age. The study found that sella turcica presented with a three different shapes: in a U shape (50.7 %), in a J shape (32.4 %) and shallow (16.9 %). Sella shape and dimensions reported in the current study can be used for discovering pathological enlargement of the pituitary fossa and may also be helpful in providing reference data in the orthodontic diagnosis, assessment and treatment plan and assessment of racial, gender and age specific variation in the Iraqi population.

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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.