Dental Situation Prediction Method for Orthodontic Treatment Planning
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Solution Overview
Problem
Current orthodontic treatments require frequent patient visits and additional costs due to the need for multiple adjustments of orthodontic appliances, and there is a lack of effective anticipation of tooth position changes, especially during growth or aging, limiting the efficiency and accuracy of treatment planning.
Innovation Solution
A method that predicts future dental situations by analyzing historical and current data, including anatomical and functional parameters, to optimize orthodontic treatment planning and appliance adjustments, reducing the number of visits and costs, and improving treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If frequent orthodontic controls and adjustments are performed, then treatment accuracy is improved, but patient time loss and treatment cost increase
Solution Approach 1:
The system performs preliminary actions by predicting future tooth positions and treatment outcomes before actual orthodontic controls occur. The prediction system simulates multiple future scenarios to determine optimal control timing, allowing the orthodontist to schedule adjustments only when necessary rather than following a fixed frequent schedule.
Solution Approach 2:
The prediction system acts as a self-service tool for the orthodontist, automatically analyzing treatment progress and generating optimization recommendations without requiring manual assessment for every control visit. The system serves itself by continuously monitoring treatment data and proactively identifying when adjustments are needed.
2Manufacturing precision
If multiple orthodontic controls and adjustments are performed, then treatment accuracy is improved, but treatment cost increases
Solution Approach 1:
The system performs preliminary cost analysis by predicting which controls and adjustments will actually improve treatment outcomes. By simulating future treatment paths, the system identifies unnecessary controls that would increase cost without providing benefit, allowing the orthodontist to eliminate wasteful expenditures before they occur.
Solution Approach 2:
The prediction system provides self-service cost optimization by automatically analyzing treatment data and generating recommendations that reduce unnecessary spending. The system monitors treatment progress and proactively identifies cost-saving opportunities, such as extending intervals between controls when treatment is proceeding as planned.
3Productivity
If orthodontic treatment is accelerated, then treatment duration is reduced, but treatment complexity and risk of errors increase
Solution Approach 1:
The system applies dynamics by continuously adapting the treatment plan based on real-time prediction results. As treatment progresses, the system dynamically adjusts control timing and appliance parameters to maintain optimal acceleration while managing complexity. The prediction model evolves with treatment data, becoming more accurate and enabling faster treatment with controlled risk.
Solution Approach 2:
The system implements feedback loops where treatment outcomes are continuously monitored and fed back into the prediction model. This feedback mechanism allows the system to learn from actual treatment responses and adjust acceleration strategies accordingly, reducing errors by identifying when accelerated treatment is approaching unsafe limits.
4Adaptability or versatility
If traditional orthodontic treatment planning is used, then treatment follows conventional protocols, but ability to anticipate tooth position changes is limited
Solution Approach 1:
The system performs preliminary action by predicting future tooth positions and treatment outcomes before they actually occur. By simulating multiple future scenarios based on current treatment data, the system provides the orthodontist with advance information about potential treatment paths, enabling proactive decision-making rather than reactive adjustments.
Solution Approach 2:
The system implements continuous feedback by monitoring treatment progress and comparing actual outcomes with predicted outcomes. This feedback loop enhances anticipation capability by continuously updating the prediction model with real treatment data, allowing the system to adapt to individual patient responses and improve future predictions.
Data Source
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AI summary
A system for predicting a future dental situation for a current patient, comprising: - a computer database, on which historical data are recorded; - means for acquiring data for the acquisition, at a current time, of data relating to a dental situation experienced by said current patient; - a computer program comprising instructions which leads it to implement the following steps: construction of a predictive model allowing to predict how a value of a tooth positioning parameter will evolve, determination of two curves (Tmax, Tmin) providing the temporal evolution of said positioning parameter up to a future objective time (tf) and defining minimum and maximum limits considered acceptable for said positioning parameter, representation of the two curves on the same graph.