A Virtual Patient-Based Workflow for Mandibular Stabilization Using a Virtual Articulator and Digital Splint.
Authors
Affiliations (12)
Affiliations (12)
- Department of Orthodontics, School of Dentistry, Chonnam National University, Gwangju, South Korea.
- Catholic University, Seoul, South Korea.
- Private Practice, Gwangju, South Korea.
- Postgraduate Orthodontic Program, Arizona School of Dentistry & Oral Health, A.T. Still University, Mesa, Arizona, USA.
- Graduate School of Dentistry, Kyung Hee University, Seoul, South Korea.
- Daegu MIR Dental Hospital, Daegu, Republic of Korea.
- Graduate School of Dentistry, Chonnam National University, Gwangju, South Korea.
- Private Practice, Goyang, South Korea.
- Department of Orthodontics, College of Dentistry, Dankook University, Cheonan, South Korea.
- Roth Orthodontic Society, Seoul, South Korea.
- Kyung Hee University, Seoul, South Korea.
- Private Practice, Yeosu, South Korea.
Abstract
To present a fully digital workflow for mandibular stabilization that reproduces the conventional articulator-based process within a virtual environment, integrating patient-specific three-dimensional (3D) datasets and artificial intelligence (AI)-assisted alignment. The workflow involves registration of intraoral scans, cone-beam computed tomography (CBCT), and/or facial scans to construct a unified virtual patient. Mandibular casts are mounted in centric relation on a virtual articulator using reference best-fit alignment. A virtual face-bow transfer aligns the maxillary cast to the articulator orientation plane based on anatomical landmarks from CBCT or facial scans. Stabilization splints are digitally designed with adjustable condylar guidance and anterior ramp inclination and fabricated via liquid crystal display (LCD) 3D printing with optimized orientation and post-processing. Clinical follow-up includes repeated digital centric relation bite registrations to monitor condylar position and confirm mandibular stability. The fully digital workflow achieves mandibular stabilization while maintaining the biomechanical objectives of conventional methods. AI-assisted alignment, computer-aided design (CAD)-based splint design, and validated 3D printing parameters enhance precision, reproducibility, and clinical efficiency. This protocol provides a reproducible and clinically feasible approach to mandibular stabilization, bridging conventional occlusal principles with digital implementation, and establishing a foundation for future AI-integrated occlusion-centered therapy.