Development of Artificial Neural Network Model for Predicting the Rapid Maxillary Expansion Technique in Children with Cleft Lip and Palate

Mohamed Zahoor Ul Huqh ¹ Johari Yap Abdullah ^2,3* Adam Husein ^4,5 Matheel AL-Rawas ⁴ Wan Muhamad Amir W Ahmad ⁶ Nafij Bin Jamayet ⁷ Mohammad Khursheed Alam ⁸ Mohd Rosli Bin Yahya ⁹ Siddharthan Selvaraj ^10,11* Abedelmalek Kalefh Tabnjh ^12,13,3*

• ¹ International Research Fellow, Faculty of Dentistry, Petaling Jaya, Selangor, Malaysia
• ² Craniofacial Imaging Laboratory, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu, Malaysia
• ³ Dental Research Unit, Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
• ⁴ Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu,, Malaysia
• ⁵ University of Sharjah, College of Dental Medicine, Department of Preventive and Restorative Dentistry, Sharjah, United Arab Emirates
• ⁶ Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu,, Malaysia
• ⁷ Division of Clinical Dentistry (Prosthodontics), School of Dentistry, International Medical University, Bukit Jalil, Kuala Lumpur, Malaysia
• ⁸ Orthodontic Division, College of Dentistry, Jouf University, Sakaka, Saudi Arabia
• ⁹ Oral & Maxillofacial Surgery Department, Hospital Raja Perempuan Zainab II, Kota Bharu, Kelantan Darul Naim, Malaysia
• ¹⁰ Department of Public Health Dentistry, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
• ¹¹ Department of Dental Research Cell, Dr.D.Y. Patil Dental College & Hospital, Dr.D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
• ¹² Department of Cariology, Odontology School, Sahlgrenska Academy,, University of Gothenburg, Gothenburg, Sweden
• ¹³ Department of Applied Dental Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Irbid, Jordan

The 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 nonsurgical rapid maxillary expansion (RME) in children with unilateral cleft lip and palate (UCLP). A 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. The 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). The 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.