Application of AR and VR in Dental Science Using CNN and the Marching Cubes Algorithm

Abstract

This paper presents a comprehensive study on the application of Augmented Reality (AR) and Virtual Reality (VR) in the dental field, integrating advanced computational techniques such as Convolutional Neural Networks (CNNs) for precise image segmentation and the Marching Cubes algorithm for high-resolution 3D reconstruction. The proposed system significantly improves dental education, diagnostic procedures, and treatment planning by creating immersive and interactive environments. CNNs are utilized to enhance the accuracy of dental image analysis, while the Marching Cubes algorithm enables the generation of detailed, realistic 3D models of oral anatomy. This dual approach addresses key limitations in traditional dental training and clinical workflows, fostering a deeper understanding of anatomical structures and increasing the efficiency of diagnosis and treatment. Experimental evaluations reveal substantial improvements in segmentation accuracy and user engagement, demonstrating the system\'s potential to revolutionize both academic and clinical practices in modern dentistry through AR/VR integration.

Introduction

The dental industry is undergoing a significant transformation with the integration of Augmented Reality (AR), Virtual Reality (VR), and AI-driven technologies. These immersive tools enhance clinical diagnostics, treatment planning, and dental education by providing interactive, 3D environments. Key technologies include Convolutional Neural Networks (CNNs) for accurate segmentation of dental structures from radiographs and the Marching Cubes algorithm for converting segmented data into high-fidelity 3D models.

In education, these technologies offer realistic simulations, improving student engagement and skill acquisition. Clinically, they support more accurate diagnoses and personalized treatment by enabling detailed anatomical visualization and real-time surgical guidance.

The literature supports these advances, showing success in applications like VR-based simulators and AR-guided surgeries. However, challenges such as manual segmentation, latency, registration errors, and lack of real-time model updates persist in existing systems.

To address these, the proposed system fully automates the process:

• CNNs segment dental scans (e.g., CBCT, panoramic X-rays).

• Marching Cubes reconstructs the data into 3D models.

• These models are used in AR (e.g., Microsoft HoloLens) and VR (e.g., Oculus Rift) platforms for clinical and educational use.

Benefits include:

• Real-time, patient-specific 3D visualization.

• Improved diagnostic accuracy and surgical planning.

• Enhanced student learning through immersive simulation.

The system also responds to the global burden of oral diseases, which affect 3.5 billion people. Conditions like dental caries, periodontal disease, and oral cancers are often linked to socioeconomic disparities and poor access to care. AR/VR combined with AI can support early diagnosis, personalized treatment, and broader access to quality dental training—especially in underserved regions.

Conclusion

The authors wish to express their profound appreciation to Dentist Dr. Jeenu Sarah Kurian of Mitra Muti Speciality Dental Clinic and Pharmacologist Drx. Benji K Simon of Brosco Pharmacy for their generous provision of clinical datasets and expert consultation, which formed the cornerstone of this research. Our sincere thanks extend to the dedicated development team, meticulous data annotators, and skilled technical staff whose tireless efforts brought the AR/VR framework to fruition. This work was made possible through the support of Hyderabad Institute of Technology and Management, for which we are deeply grateful. We are indebted to the pioneering researchers whose published works in this field provided essential data, algorithms, and methodological insights. Their scholarly contributions have been instrumental in shaping our approach. This achievement truly represents the synergy of diverse disciplines - where artificial intelligence, advanced visualization technologies, and clinical dentistry converge to create meaningful innovations. Finally, we acknowledge all contributors, both named and unnamed, whose collective wisdom and support made this interdisciplinary endeavour possible.

References

[1] World Health Organization. (2022). Oral Health Report. [2] Acharya, S., et al. (2021). \"Haptic Feedback in VR Dental Simulators.\" J Dent Educ. [3] Dabbagh, A., et al. (2020). \"Applications of AR in Dental Education.\" Int J Med Sci. [4] Gauthier Dot, Akhilanand Chaurasia(2024) DentalSegmentator: robust deep learning-based CBCT image segmentation [5] Patel, N., et al. (2022). \"AR-Guided Dental Implant Surgery.\" Clin Oral Implants Res. [6] Tong, H., et al. (2020). \"Interactive VR Simulations for Oral Pathology.\" Virtual Med J. [7] Choi, J. Y., et al. (2019). \"HoloLens for AR-Guided Dental Procedures.\" J Prosthet Dent. [8] Hansen and Johnson, (2004). Visualization Handbook, Edition 1 [9] Xu, Y., et al. (2022). \"Real-Time AI-assisted Dental Diagnostics.\" IEEE Access. [10] Lee, C., et al. (2020). \"Visualizing Maxillofacial Structures in VR.\" 3D Imaging Med. [11] Zhang, L., et al. (2018). \"Segmentation Accuracy in Dental Imaging Using Deep Learning.\" AI Dent Res. [12] IBM Cloud Healthcare Compliance. (2023). \"HIPAA/GDPR Standards in Medical Data.\" [13] Lorensen, W. & Cline, H. (1987). \"Marching Cubes: A High-Resolution 3D Surface Construction Algorithm.\" ACM SIGGRAPH. [14] The use of CNNs in VR/AR/MR/XR: a systematic literature review (2024) David Cortes, Belen Bermejo & Carlos Juiz [15] 2014, Accelerating MATLAB with GPU Computing Jung W. Suh, Youngmin Kim a https://www.sciencedirect.com/topics/computer-science/marching-cube-algorithm [16] Buchanan, H, Niekerk (2001) “Assessing fieldwork journals: developmental portfolios”. British Journal of Occupational Therapy. [17] Ronneberger, O., et al. (2015). \"U-Net: Convolutional Networks for Biomedical Image Segmentation.\" [18] Yamamoto, K., et al. (2021). \"Tele-VR Training in Oral Surgery.\" J Med Educ Technol.

Copyright

Copyright © 2025 Bobby K Simon, Jeenu Sarah Kurian, Benji K Simon. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.