AI Dental Prosthesis Planning With Standardized Facial Landmarks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current dental prostheses planning systems fail to standardize patient head position in three-dimensional space, neglect age, gender, and ethnicity in smile patterns, and lack accurate bone reduction methods, leading to potential prosthesis or implant failures and increased OR time.
Innovation Solution
A system utilizing artificial intelligence to standardize photographic records, calculate ideal dental implant positions, and design personalized prostheses based on facial proportions, age, and ethnicity, incorporating machine learning to ensure proper esthetics, phonetics, hygiene, and occlusion, and enabling efficient provisional prosthesis attachment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current bone reduction methods are used, then bone reduction is performed, but implant failure rate increases due to not considering patient-specific landmarks
Solution Approach 1:
The system performs preliminary digital planning and simulation of bone reduction and implant placement before actual surgery. Virtual models are created from patient scans, allowing the treatment plan to be optimized in advance, ensuring proper bone reduction that preserves patient-specific landmarks while achieving ideal implant positions.
Solution Approach 2:
The system uses photogrammetric analysis to capture and analyze patient facial features, providing feedback on smile patterns, lip dynamics, and ethnic characteristics. This feedback loop allows continuous refinement of the treatment plan to match patient-specific anatomy and aesthetic goals, improving both reliability and adaptability.
2Manufacturing precision
If physical grinding of provisional prosthesis is performed in the operating room, then prosthesis fit is achieved, but operating room time increases significantly
Solution Approach 1:
The provisional prosthesis is pre-adjusted and pre-fitted in the digital planning phase before the patient enters the operating room. Virtual try-in allows precise adjustment of prosthesis position, orientation, and morphology in advance, so that minimal or no adjustment is needed during surgery, dramatically reducing OR time while maintaining high precision.
Solution Approach 2:
The system creates a digital copy of the patient's oral anatomy and uses this virtual model to design and adjust the provisional prosthesis. This digital replica allows all necessary adjustments to be made virtually before fabrication, eliminating the need for time-consuming physical grinding in the operating room.
3Ease of operation
If standard photographic records are used without head position standardization, then photo collection is simple, but diagnostic accuracy decreases
Solution Approach 1:
The system establishes a standardized head position reference frame (equipotential reference) that all photographic records are aligned to. By defining a consistent coordinate system based on facial landmarks, the system ensures that photos taken at different times and angles can be accurately compared and measured, maintaining diagnostic precision while keeping the process simple through automated alignment.
4Measurement precision
If AI-based standardized photo collection is implemented, then diagnostic accuracy improves, but system complexity increases
Solution Approach 1:
The AI system automatically performs photogrammetric analysis, landmark identification, and head position standardization without requiring manual intervention. The system self-calibrates and self-adjusts, using machine learning algorithms to recognize facial features and optimize photo quality, thereby improving diagnostic accuracy while minimizing the operational complexity for the user.
Data Source
AI summary
Methods, systems, and techniques for collecting data for use in designing a personalized dental prosthesis for a patient. At least one camera is used to obtain a series of two-dimensional photos or a three-dimensional model of a head and face of the patient. At least one machine learning model is used to determine facial or oral landmarks and a central incisal edge of the prosthesis from the photos or model. Dimensions for the dental prosthesis are determined form the landmarks and central incisal edge. The dimensions include a labial border of the prosthesis, distal borders of the prosthesis, a superior border of the prosthesis, an inferior border of the prosthesis, a lingual border of the prosthesis, and buccal borders of the prosthesis. The dimensions are output to an output file for use in manufacturing the prosthesis.