AUTHOR=Zahoor Ul Huqh Mohamed , Abdullah Johari Yap , Husein Adam , AL-Rawas Matheel , W. Ahmad Wan Muhamad Amir , Jamayet Nafij Bin , Alam Mohammad Khursheed , Bin Yahya Mohd Rosli , Selvaraj Siddharthan , Tabnjh Abedelmalek Kalefh 

TITLE=Development of artificial neural network model for predicting the rapid maxillary expansion technique in children with cleft lip and palate

JOURNAL=Frontiers in Dental Medicine

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2025.1530372

DOI=10.3389/fdmed.2025.1530372

ISSN=2673-4915

ABSTRACT=AimThe study aimed to determine the mid-palatal suture (MPS) maturation stages and to develop a binary logistic regression model to predict the possibility of surgical or non-surgical rapid maxillary expansion (RME) in children with unilateral cleft lip and palate (UCLP).MethodsA retrospective case control study was conducted. A total of 100 subjects were included. Data was gathered from the databases of Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab II, respectively. Cone beam computed tomography scans of both cleft and non-cleft individuals were utilized to determine the MPS maturation stages. Romexis software version 3.8.2 was used to analyze the images.ResultsThe results of the binary logistic regression model were utilized to establish the relationship between the probability (P) of a specific event of interest (P(Y = 1)) and a linear combination of independent variables (Xs) using the logit link function. Potential factors such as age, gender, cleft, category of malocclusion, and MPS were chosen which could play a role in predicting the technique of RME in children with UCLP and non-UCLP. A subset of these variables was validated via multilayer feed forward neural network (MLFFNN).ConclusionsThe effectiveness of the hybrid biometric model created in this work, which combines bootstrap and BLR with R-syntax was evaluated in terms of how accurately it predicted a binary response variable. A validation method based on an MLFFNN was used to evaluate the precision of the generated model. This leads to a good outcome.