Patient-Specific Dental Prosthesis for Predictable Gingival Contouring
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Solution Overview
Problem
Current dental prosthesis design systems lack the ability to predict soft tissue outcomes for individual patients, relying on general experiences rather than specific patient data, which can lead to unpredictable healing and aesthetic results.
Innovation Solution
A method that utilizes scan data from current patients to identify clinical factors and desired soft tissue outcomes, accessing a database of previous patients' information to select and modify design features from similar cases, ensuring a patient-specific prosthesis design that mimics successful soft tissue outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general experience-based design methods are used, then design simplicity is maintained, but soft tissue outcome predictability deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing soft tissue outcome data from previous patients in a database before designing the current prosthesis. This pre-established database enables predictive modeling by comparing current patient parameters with historical data, thereby improving outcome predictability without requiring complex real-time analysis during the design process.
Solution Approach 2:
The system creates a virtual copy of the patient's oral anatomy and soft tissue characteristics through digital scanning and modeling. This virtual model is then used to simulate and predict soft tissue outcomes by comparing with historical data from previous patients, allowing the design to be optimized based on proven successful outcomes without requiring physical trial-and-error approaches.
2Measurement precision
If patient-specific predictive modeling is implemented, then soft tissue healing accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the most relevant parameters from comprehensive patient data, such as key soft tissue characteristics, anatomical measurements, and clinical outcomes from previous patients. By focusing on essential parameters rather than processing all available data, the system achieves high measurement precision while minimizing information processing requirements and avoiding data overload.
Solution Approach 2:
The system transforms complex clinical data into standardized parameters that can be efficiently stored and compared in the database. By converting diverse soft tissue measurements into comparable parameter formats, the system enables accurate predictive modeling while reducing the complexity of data processing and storage requirements.
3Reliability
If historical patient data is utilized, then design reliability is improved, but system complexity increases
Solution Approach 1:
The database system is designed with multi-functionality to handle various types of patient data (clinical measurements, soft tissue characteristics, treatment outcomes) in a unified structure. This universal database can store and retrieve information for multiple patients across different case types, improving design reliability through comprehensive historical data while avoiding the need for separate complex systems for each data type.
Data Source
AI summary
A method of designing a patient-specific prosthesis for a current patient includes receiving scan data of a mouth of the current patient to identify conditions at a location at which the patient-specific prosthesis is to be placed on a dental implant, and determining at least two clinical factors for the current patient. The method further includes identifying a desired outcome for soft tissue for the current patient at the location, and accessing a database having soft-tissue-outcome information for each of a plurality of previous patients. The database further includes clinical-factor information for each of the plurality of previous patients. Based on the soft-tissue-outcome information and the clinical-factor information for at least one of the plurality of previous patients being related to the current patient's desired outcome and the current patient's at least two clinical factors, the method includes developing a design for the patient-specific prosthesis for the current patient.