Dynamic Vision Prediction Model for Myopia Progression
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Solution Overview
Problem
Existing vision correction devices are standardized and fail to account for individual lifestyle, behavior, and environmental changes, leading to inconsistent and erroneous predictions of myopia progression over time.
Innovation Solution
A method using a prediction model built from data collected from a group of individuals, which processes successive measurements of vision-related parameters to provide a dynamic and accurate prediction of vision evolution, considering factors like chronotype and time-dependent environmental parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized devices are used for all persons, then device complexity is reduced and ease of manufacture is improved, but prediction accuracy deteriorates because individual lifestyle, behavior, and environmental changes are not accounted for
Solution Approach 1:
The prediction model is designed to be dynamic rather than static. It automatically updates predictions when new measurements are received, adapting to changes in the person's lifestyle, behavior, and environment over time. This dynamic approach resolves the contradiction by maintaining high prediction accuracy through continuous adaptation without requiring complex manual reconfiguration of the device.
Solution Approach 2:
The system implements feedback mechanisms where successive measurements are fed back into the prediction model to continuously refine and update predictions. This feedback loop allows the model to learn from new data and adjust to individual changes, achieving high prediction accuracy without requiring complex manual intervention or device reconfiguration.
2Reliability
If a prediction model is calculated once and not updated, then device complexity is reduced and ease of operation is improved, but reliability deteriorates when the person's lifestyle, behavior, or environment changes
Solution Approach 1:
The prediction model performs self-updating automatically when new measurements are received, without requiring manual intervention or complex external management. The system serves itself by automatically incorporating new data to maintain prediction consistency, resolving the contradiction between reliability and device complexity through autonomous operation.
Solution Approach 2:
The model transitions from a static once-calculated approach to a dynamic automatically-updating approach. This dynamic characteristic enables the model to maintain reliability over time by adapting to changes in the person's lifestyle, behavior, and environment, while the automation of updates prevents the system from becoming overly complex.
3Measurement precision
If successive measurements are jointly processed with differential weighting, then prediction accuracy is enhanced by accounting for chronotype and time-dependent factors, but measurement and detection difficulty increases
Solution Approach 1:
The patent replaces complex manual analysis and mechanical processing with automated computational methods. The differential weighting of successive measurements based on chronotype and time-dependent factors is performed automatically by the prediction model, achieving high prediction accuracy without requiring difficult manual detection and measurement of these subtle temporal patterns.
Data Source
AI summary
This method for predicting evolution over time of at least one vision-related parameter of at least one person includes: obtaining successive values for the person, respectively corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the person; predicting by at least one processor the evolution over time of the vision-related parameter of the person from the obtained successive values for the person, by using a prediction model associated with a group of individuals; the predicting including associating at least part of the successive values for the person with the predicted evolution over time of the vision-related parameter of the person, the associating including jointly processing the successive values associated with the same parameter of the first predetermined type. The predicted evolution depends differentially on each of the jointly processed values.