Evaluation of Sella Turcica Bridging and Morphology in Different Types of Cleft Patients

Abstract

Objectives: To evaluate sella turcica (ST) bridging, associated anomalies, and morphology, in subjects with four different types of clefts, and compare them with non-cleft (NC) subjects. Materials and Methods: A total of 123 (31 NC and 92 cleft) Saudi subjects who had their lateral cephalogram (Late. Ceph.), orthopantomogram (OPG), and clinical details for ordinary diagnosis were included in the study. Among 92 cleft subjects, 29 had bilateral cleft lip and palate (BCLP), 41 had unilateral cleft lip and palate (UCLP), nine had unilateral cleft lip and alveolus (UCLA), and 13 with unilateral cleft lip (UCL). ST bridging and seven parameters related to ST morphology and skeletal malocclusion were analyzed using Late. Ceph. Associated dental anomalies in ST bridging subjects were investigated using OPG. The images were investigated using artificial intelligence driven Webceph software. Multiple statistical tests were applied to see the differences between gender and among cleft vs NC subjects. Results: ST bridging was found to be higher in cleft subjects (22.82%). Most of the cleft subjects had severe skeletal Class III malocclusion associated with multiple types of dental anomalies (impacted canines, congenital missing, and presence of supernumerary teeth). No significant gender disparities in all seven parameters of ST morphology were found between NC and cleft groups. However, there were significant differences when compared among four different types of cleft individuals vs NC subjects. Conclusion: ST bridging is more prevalent in cleft subjects along with Class III malocclusion and associated dental anomalies. ST morphometry differs significantly between cleft vs NC subjects. BCLP exhibits smaller values of all seven parameters as compared to all other groups.

Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery

“…The landmarks of the patients’ cephalometric radiographs were automatically detected with a software program using a gradient boosting algorithm (WebCeph, AssembleCircle, Seoul, Korea). WebCeph has been used for landmark detection in several studies [ 24 , 25 ]. The minimum box, including all the landmarks, was selected with an external margin of 5% and the lower part of the box was selected by square cropping.…”

Sagittal Jaw Relationship of Different Types of Cleft and Non-cleft Individuals

“…Artificial intelligence (A.I.) into dentistry, especially in cleft research is still a new-fangled and robust technique (18)(19)(20)(21).…”

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

“… Alam et al (2019) , Alam and Alfawzan (2020) investigated the craniofacial morphology of Japanese UCLP patients and investigated the association with congenital (2019) and postnatal treatment factors (2013). Among congenital factors, gender and DC (U1-SN) showed insignificant disparities, which coincide with the results of the present study.…”

“…Numerous strategies for the evaluation of the craniofacial characteristics, dental relationship, and maxillary morphometry measurement of CLP individuals have been depicted already ( Alam et al, 2008 , 2013 , 2019 ; Kajii et al, 2013 ; Asif et al, 2016 ; Arshad et al, 2017a , b , 2018 ; Haque et al, 2017a , b , 2018 ). The result of the craniofacial characteristics of CLP can be evaluated from multifacets of factors, for example, dental relationship ( Haque et al, 2018 ), cephalogram ( Alam et al, 2013 , 2019 ; Wu et al, 2013 ; Batwa et al, 2018 ; Alam and Alfawzan, 2020 ), cone-beam computed tomography ( Parveen et al, 2018 ), and maxillary morphometry ( Haque et al, 2020 ). Oral clefts show an assortment of clinical inconsistencies ( Batwa et al, 2018 ).…”