Persistent Deciduous Teeth Risk Prediction Algorithm
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Solution Overview
Problem
Persistent deciduous teeth (PDT) in pets pose issues such as malocclusion, soft tissue trauma, and increased risk of periodontal disease, and existing methods lack efficiency in predicting and mitigating these risks based on pet attributes.
Innovation Solution
A method and system for predicting the risk of PDT in pets by receiving pet data, determining attribute weights for each pet attribute, analyzing these values using a PDT risk level prediction algorithm, and displaying the predicted risk level on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to monitor pet dental health, then veterinarians can provide general care, but early detection of persistent deciduous teeth risk is delayed and accuracy is reduced
Solution Approach 1:
The system performs preliminary risk assessment by analyzing pet attributes (breed, size, age) before clinical symptoms manifest. The PDT risk prediction algorithm calculates risk levels in advance, enabling veterinarians to proactively monitor high-risk pets and intervene before persistent deciduous teeth cause malocclusion or periodontal disease.
Solution Approach 2:
The patent replaces traditional mechanical visual inspection with an automated digital risk prediction system. The algorithm processes pet attribute data computationally to generate risk scores, substituting manual veterinary assessment with an objective, scalable digital evaluation method that improves both accuracy and efficiency.
2Measurement precision
If comprehensive pet data analysis is implemented to improve PDT risk prediction, then prediction accuracy increases, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the PDT risk assessment into distinct components: pet attribute data collection (breed, size, age), risk factor weighting, and algorithmic risk level calculation. This modular segmentation allows each component to be optimized independently while maintaining overall system accuracy and manageability.
Solution Approach 2:
The patent utilizes parameter changes by transforming qualitative pet attributes (breed categories, size classifications) into quantitative risk scores through the prediction algorithm. Different attribute parameters are weighted and combined to generate a comprehensive risk level, enabling accurate assessment while maintaining computational efficiency.
Data Source
AI summary
Various embodiments of this disclosure relate generally to predicting a risk level for the presence of persistent deciduous teeth (PDT) for one or more pets. The method comprises receiving, by one or more processors, pet data corresponding to a pet from a user device, the pet data including one or more pet attributes, based on the one or more pet attributes, determining, by the one or more processors, a result value indicating a PDT attribute weight for each of the one or more pet attributes, analyzing, by the one or more processors, the result value for each of the one or more pet attributes to determine a PDT risk level, the analyzing including utilizing a PDT risk level prediction algorithm, and displaying, by the one or more processors, the PDT risk level on one or more user interfaces of the user device.