3D Assessment of Mandibular Bone Loss Via STL Superimposition After Complete Denture Occlusal Adjustment

3D Quantification Assessment of Mandibular Bone Resorption Using STL Registration-Based Superimposition Following Removable Complete Denture Occlusal Equilibration

STL registration-based superimposition is an advanced technique for assessing mandibular bone resorption in removable complete denture (RCD) patients. This 3D method involves aligning and comparing digital models of the mandible before and after RCD use, offering high accuracy and comprehensive analysis. The process includes 3D scanning, STL conversion, registration, superimposition, and quantification. Occlusal equilibration of RCDs plays a crucial role in distributing masticatory forces and influencing bone resorption patterns. Studies using this technique have revealed non-uniform resorption, with variations in different regions of the mandible. While offering numerous advantages over traditional methods, challenges include potential registration errors and the need for specialized equipment and expertise. Future developments may incorporate machine learning, biomechanical modeling, and long-term studies to enhance understanding and clinical application of this technology in prosthodontics and oral surgery.

STL registration-based superimposition is an innovative technique for evaluating mandibular bone resorption in patients using removable complete dentures (RCDs). This 3D method aligns and compares digital mandible models from different time points, providing precise volumetric and surface change measurements. The process involves 3D scanning, STL file conversion, model registration, superimposition, and quantification.

Occlusal equilibration of RCDs is crucial for distributing masticatory forces evenly, influencing bone resorption patterns. Research using this technique has shown that mandibular bone resorption is non-uniform, with variations across different mandibular regions.

While offering superior accuracy and comprehensive analysis compared to traditional methods, this approach faces challenges such as potential registration errors and the need for specialized equipment and expertise. Future developments may incorporate machine learning, biomechanical modeling, and longitudinal studies to enhance understanding and clinical application in prosthodontics and oral surgery.

Inclusion Criteria

• Completely edentulous patients ranging from age 45 to 75 years
• Angle's Class I skeletal relationship
• Normal facial symmetry
• Cooperative patients
• Adequate inter-arch space not less than 12mm

Exclusion Criteria

• Temporomandibular disorders
• Uncontrolled diabetes
• Bleeding disorders or anticoagulant therapy
• Flabby tissues or sharp mandibular residual ridge.
• Heavy smokers.
• Patient's with neuromuscular disorders
• Patients on chemotherapy or radiotherapy
• Severe psychiatric disorders
• Angle's class II and III skeletal relationship