Development of 3D Artificial Intelligence for maxillofacial morphology identification

Ardani, I Gusti Aju Wahju and Wahyudi, Riezki Dwianggraini and Halim, Olivia and Caesar, Aya Dini Oase and Gautama, Shirley and Narmada, Ida Bagus and Winoto, Ervina Restiwulan and Fajar, Aziz (2025) Development of 3D Artificial Intelligence for maxillofacial morphology identification. World Journal of Advanced Research and Reviews, 26 (3). 099-109. ISSN 2581-9615

Introduction: Artificial Intelligence (AI) in 3D image analysis using Cone Beam Computed Tomography (CBCT) can be used to provide accurate and reliable orthodontic diagnosis. When deciding on an orthodontic treatment plan and evaluation, it is crucial to identify the maxillary and mandibular morphology. Objectives: To identify the maxillary and mandibular morphology of patients with malocclusion at the Dental Hospital Universitas Airlangga’s Orthodontic Specialist Clinic using AI. This project is a preliminary study into the development of 3D AI-based digital tracing software. Material and methods: A total of 17 CBCT x-rays of class I malocclusion patients with Javanese ethnicity were divided into training and validation samples. After being manually annotated, the training samples were loaded into deep learning software. Deep learning using Convolutional Neural Network (CNN) is repeated until the manual annotation points and prediction points reach the most accurate coordinates. The results were validated using the validation samples. Results: The lowest MSE in maxillary morphology is at the as point (808.4) and the highest is at the ANS point (3043.8), while in mandibular morphology, the lowest MSE is at the Pg point (927) and the highest is at the Cd-MR point (8675). Even though there are still a number of anatomical landmark locations with high error rates, the outcomes of deep learning are fairly acceptable. Conclusion: CNN-based AI deep learning models can be used to identify maxillary and mandibular anatomical landmarks on CBCT x-rays, however additional data are still required to maximize the deep learning outcomes.