Asynchronous Gingiva Strip Processing for Dental Model Accuracy
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Solution Overview
Problem
The existing orthodontic treatment planning process is time-consuming and complex, involving many manual steps that require significant expertise, and is prone to errors due to artifacts like attachments on patients' teeth, which can lead to suboptimal treatment plans.
Innovation Solution
The development of systems and methods for automatically detecting and removing attachments from 3D model data, allowing for asynchronous processing of treatment plan modifications, which includes identifying attachments using machine learning models and presenting modified model data to dental professionals for review and confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual steps are used for treatment planning, then expertise and control are maintained, but the process is time-consuming and complex
Solution Approach 1:
The system performs preliminary automated processing of 3D model data, including attachment detection and removal, before the dental professional reviews the treatment plan. This preliminary action prepares the data in advance, reducing the time the professional needs to spend on manual processing while maintaining plan accuracy through subsequent review.
Solution Approach 2:
The system creates copies of the 3D model data and processes these copies asynchronously through attachment detection and removal algorithms. This allows the original data to remain available for professional review while processed versions are prepared in parallel, significantly reducing overall processing time without sacrificing accuracy.
2Loss of information
If attachments are not removed from 3D model data, then complete patient information is preserved, but treatment plans become suboptimal due to artifacts
Solution Approach 1:
The system segments the 3D model data to identify and separate attachments from the actual dental structures. By detecting attachments as distinct objects and removing them selectively, the system preserves all relevant patient information while eliminating only the harmful artifacts that would compromise treatment plan quality.
Solution Approach 2:
The system extracts and removes attachments from the 3D model data using automated detection algorithms. This extraction process selectively removes only the attachment artifacts while preserving all other patient-specific anatomical features, ensuring both data completeness and treatment plan reliability.
3Reliability
If synchronous processing is used for treatment plan modifications, then all changes are reviewed together, but processing time increases
Solution Approach 1:
The system performs attachment detection and removal as preliminary actions before the dental professional reviews the treatment plan. These processing steps complete in advance, allowing the professional to focus only on review and confirmation, thereby maintaining review accuracy while significantly improving processing speed.
Solution Approach 2:
The system implements asynchronous processing where attachment detection, removal, and model modification occur continuously in the background without blocking the professional review process. This continuous processing maintains productivity improvements while ensuring all changes are available for comprehensive review when needed.
Data Source
AI summary
Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.