Treatment Outcome and Factors Affecting Dental Arch Relationship in Malay Children with Unilateral Cleft Lip and Palate (UCLP)

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 Assessment of 3D Digital Models Using GOSLON Yardstick Index: Exploring Confounding Factors Responsible for Unfavourable Treatment Outcome in Multi-Population Children With UCLP

To evaluate dental arch relationship (DAR) using GOSLON Yardstick and also to explore the association between multiple factors (age, gender, UCLP types, UCLP side, Family history of cleft, family history of Class III malocclusion, techniques of cheiloplasty, techniques of palatoplasty) and DAR in children unilateral cleft lip and palate (UCLP) in different populations. Two hundred fifty-five laser scanned 3D digital models (LS3DM) of UCLP children (5-12 years) from Malaysia, Bangladesh, and Pakistan were included. The intra- and inter-examiner agreements were evaluated by kappa statistics, to compare the GOSLON mean score between the populations and to explore the responsible factors that affect DAR, one way ANOVA, and crude logistic regression analysis was used, respectively. The mean GOSLON score was 2.97; 3.40 and 3.09 in Malaysia, Bangladesh, and Pakistan, respectively. Twenty seven, 40, and 30 subjects were in unfavourable (category rating 4 and 5) groups in Malaysia, Bangladesh, and Pakistan, respectively. A significant association was found between techniques of palatoplasty (p = 0.03; p = 0.04 and p = 0.04 in Malaysia, Bangladesh, and Pakistan, respectively) and unfavourable DAR. Different cheiloplasty techniques (p = 0.04) and gender (p = 0.03) also exhibited noteworthy associations with unfavourable DAR in the Bangladeshi population. Bardach techniques of palatoplasty were significantly associated with unfavourable DAR in all three populations. Moreover, male UCLP and modified Millard techniques of cheiloplasty were also associated with unfavourable DAR in the Bangladeshi population.

Dental Characteristics of Different Types of Cleft and Non-cleft Individuals

Objective

The objective of this study was to compare the novel artificial intelligence (A.I.)-driven lateral cephalometric (Late. Ceph.) analysis of 14 different dental characteristics (DC) among different types of cleft lip and palate (CLP) and non-cleft (NC) individuals.

Materials and Methods

A retrospective study was conducted on 123 individuals [31 = NC, 29 = BCLP (bilateral cleft lip and palate), 41 = UCLP (unilateral cleft lip and palate), 9 = UCLA (unilateral cleft lip and alveolus), and 13 = UCL (unilateral cleft lip)] with an average age of 14.77 years. Demographic details were gathered from the clinical records. A novel artificial intelligence-driven Webceph software has been used for the Late. Ceph. analysis. A total of 14 different types of angular and linear DC measurements were analyzed and compared among groups. Two-way ANOVA and multiple-comparison statistics tests were applied to see the differences between gender and among different types of CLP versus NC subjects.

Results

Of the 14 DC tested, no significant gender disparities were found (p > 0.05). In relation to different types of CLP versus NC subjects, 8 over 14 DC were statistically significant (p < 001 to p = 0.03). Six other DC variables show insignificant (p > 0.05) noteworthy alterations in relation to type of CLP.

Conclusion

Based on the results, type of CLP revealed significantly altered DC compared to NC. Among different types of CLP, BCLP exhibited a maximum alteration in different DC.