Scanner-agnostic dynamic jaw motion generation from virtual static excursive records using open-source Python-based...

Area of Science:

• Digital Dentistry
• Biomechanical Engineering
• Artificial Intelligence in Healthcare

Background:

• Current digital prosthodontic workflows often rely on static interocclusal records.
• Generating dynamic mandibular motion data is crucial for accurate digital restorations.
• Existing methods can be costly and vendor-specific.

Purpose of the Study:

• To develop a scanner-agnostic digital workflow for generating dynamic mandibular motion.
• To utilize artificial intelligence (AI) for interpolating intermediate jaw positions.
• To create a cost-effective, non-vendor-specific solution for integrating jaw motion into digital workflows.

Main Methods:

• A custom AI algorithm and user interface were developed using Python.
• Static virtual interocclusal records (maximum intercuspation, protrusion, lateral excursions) were captured using an intraoral scanner.
• Quantified point tracking and interpolation were employed to generate a motion path file compatible with CAD software.

Main Results:

• The technique successfully generated dynamic mandibular motion from static virtual records.
• The AI algorithm effectively interpolated intermediate jaw positions.
• The workflow is scanner-agnostic and compatible with existing dental CAD software.

Conclusions:

• This AI-powered digital workflow provides a cost-effective and adaptable solution for dynamic jaw motion analysis.
• It enables the integration of individualized mandibular motion into digital prosthodontic workflows.
• The use of open-source tools and AI reduces vendor dependency and enhances accessibility.