Myopia Progression Tracking System Using SPHEQ Trajectory Analysis
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Solution Overview
Problem
Current methods fail to effectively estimate and track myopia progression over time, limiting the ability of Eye Care Providers to demonstrate and understand the long-term benefits of myopia control treatments.
Innovation Solution
A method and system that estimate Spherical Equivalent Refraction (SPHEQ) trajectories for individuals by comparing their data to a reference population, allowing for the visualization of treatment benefits over time using myopia control treatments like ophthalmic lenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional corrective lenses are used to address myopia symptoms, then visual acuity is improved, but the underlying cause of myopia progression is not addressed
Solution Approach 1:
The solution segments the approach to myopia management by separating symptom correction (visual acuity) from cause treatment (axial length control). Multiple lens zones with different optical powers are used: central zone for clear vision and peripheral zones for controlling eye growth, thereby addressing both visual acuity and myopia progression simultaneously
Solution Approach 2:
Different regions of the contact lens are designed with different optical properties. The central optical zone provides clear vision for distance and near objects, while the peripheral zones introduce specific optical defocus signals to slow axial elongation. This local differentiation allows simultaneous improvement of visual acuity and control of myopia progression
2Reliability
If myopia control treatment is implemented, then long-term refractive error progression is reduced, but the ability to track and demonstrate treatment effectiveness over time is limited
Solution Approach 1:
The system implements continuous feedback by capturing refractive error measurements at multiple time points and comparing actual progression against predicted progression trajectories. This allows Eye Care Providers to objectively demonstrate treatment effectiveness by showing deviations from the expected progression path, providing visual and quantitative feedback to patients and parents
Solution Approach 2:
A computational model serves as an intermediary between treatment intervention and outcome assessment. The model predicts expected refractive error progression based on demographic data and baseline measurements, then compares this prediction with actual measurements to quantify treatment effectiveness, bridging the gap between treatment application and effectiveness demonstration
3Measurement precision
If demographic data and treatment options are collected for prediction, then personalized myopia progression estimation is improved, but system complexity increases
Solution Approach 1:
The system uses a unified computational framework that handles multiple functions: demographic data processing, baseline refraction analysis, progression prediction, actual measurement tracking, and effectiveness calculation. This multi-functional approach avoids the need for separate complex systems for each function, reducing overall system complexity while maintaining prediction accuracy
Data Source
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AI summary
A method for estimating and tracking refractive error progression of an individual includes estimating a percentile of Spherical Equivalent Refraction (SPHEQ) as a function of at least the individual's age by comparison to a reference population; estimating an expected SPHEQ trajectory over a future predetermined period of time; comparing the expected SPHEQ trajectory with the reference population; and comparing the expected SPHEQ trajectory with an expected SPHEQ trajectory using an ametropia control treatment, thereby showing a possible treatment benefit over the predetermined period of time.