Abstract
Objective: To investigate the variation between the non-syndromic cleft lip and/or palate (NSCLP) and non-cleft (NC) subjects in relation to the lip morphology (LM) and nasolabial angle (NLA). Materials and Methods: Lateral cephalogram (Late. Ceph.) of 123 individuals (92 NSCLP [29 = bilateral cleft lip and palate (BCLP), 41 = unilateral cleft lip and palate (UCLP), 9 = unilateral cleft lip and alveolus (UCLA), 13 = unilateral cleft lip (UCL)], and 31 NC) who did not undergo any orthodontic treatment were investigated. By WebCeph, an artificial intelligence- (A.I.) driven software, 2 (two) parameters of LM, namely upper lip to E line (LM-1) and lower lip to E line (LM-2), and NLA analysis was carried out for each individual. Multiple tests were carried out for statistical analysis. Results: The mean ± SD observed for LM-1, LM-2, and NLA for NC individuals were 1.56 ± 2.98, 0.49 ± 3.51, and 97.20 ± 16.10, respectively. On the other hand, the mean ± SD of LM-1, LM-2, and NLA for NSCLP individuals were 4.55 ± 4.23, 1.68 ± 2.82, and 82.02 ± 14.66, respectively. No significant variation was observed with respect to gender and side. NSCLP (different types) and NC individuals showed significant disparities in LM-1 and NLA. Conclusion: It can be concluded that parameters of lip morphology such as LM-1, LM-2, and NLA vary among NSCLP and NC individuals.
Highlights
Artificial intelligence (A.I.) is a fast-growing technology and leading in many areas of our lives
Recent scoping reviews focused on the use of A.I. in orthodontics and in cleft lip and/or palate have highlighted the growing interest of the research community in this field [1,2]
Twenty-seven subjects had unilateral cleft lip and palate (UCLP), unilateral cleft alveolus (UCLA), and unilateral cleft lip (UCL) affected on their right side, and 36 subjects were affected on their left side
Summary
Artificial intelligence (A.I.) is a fast-growing technology and leading in many areas of our lives. Artificial intelligence has been used to detect landmarks of lateral cephalograms [3,4], aid in diagnosis [5,6], and plan treatments for cases. In patients with a cleft lip and/or palate, it has been used for pre-natal diagnosis, researching its etiology, detecting landmarks, and predicting the need for surgery later in life. These systems have used multiple forms of deep learning, such as neural networks, decision trees, random forests, and k-nearest neighbor algorithms to develop A.I. models that can help orthodontists [1,2]
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