AI-Adaptive Differential Display Emphasis for Myopia Management
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Solution Overview
Problem
Existing treatments for myopia, including pharmaceutical approaches like atropine eye drops, are not well understood and may have unclear long-term effects, while non-pharmaceutical methods like screen brightness reduction can be ineffective or harmful due to individual variability and complex biological interactions, making personalized treatment difficult.
Innovation Solution
A method involving differential visual and display emphasis is applied to central and peripheral visual fields using AI-driven tools to adjust image properties like brightness and contrast, promoting therapeutic outcomes such as reducing myopia progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If atropine eye drops are used to treat myopia, then myopia progression may be counteracted to some degree, but the mechanism is not well understood making it difficult to determine suitable dose and identify contraindications, and long-term safety remains unclear
Solution Approach 1:
The patent replaces pharmaceutical intervention (atropine eye drops) with an optical system consisting of display device and AI-based decision support tool. The system uses visual stimulus control through differential display regions to induce therapeutic eye movements and accommodation responses, substituting chemical treatment with a non-pharmaceutical optical approach that avoids medication-related complexities
Solution Approach 2:
The system enables self-monitoring and self-adjustment of visual stimuli parameters through AI algorithms that analyze user responses and automatically optimize treatment parameters. The display device itself performs both treatment delivery and effect monitoring, eliminating the need for external medical intervention and simplifying the treatment ecosystem
2Adaptability or versatility
If screen brightness is decreased to avoid myopia progression, then some individuals may benefit, but the approach may be ineffective or even harmful for other individuals due to individual variability and complex biological interactions
Solution Approach 1:
The system dynamically adjusts display parameters including brightness, contrast, and regional emphasis based on real-time feedback from eye tracking and user responses. Rather than applying a static brightness reduction, the AI algorithms continuously optimize visual stimulus parameters to match individual user characteristics and treatment progression, enabling adaptive personalization of the therapeutic approach
Solution Approach 2:
The patent applies differential display emphasis to specific regions of the display rather than uniformly adjusting overall brightness. By creating distinct first and second display regions with different visual properties, the system delivers localized visual stimuli that target specific eye movements and retinal areas, providing personalized treatment based on individual anatomical and functional characteristics
3Ease of operation
If a one size fits all behavioral approach is applied to decrease screen brightness, then the approach is simple to implement, but it may not be widely effective and may even be potentially harmful
Solution Approach 1:
The system provides a universal platform that can accommodate diverse individual characteristics through a single integrated solution. The AI-based decision support tool and adaptive display system work together to personalize treatment for each user while maintaining a unified implementation approach, eliminating the need for multiple different behavioral protocols and simplifying adoption across diverse populations
4Measurement precision
If a decision matrix for diagnosis and evaluation is created to account for multiple factors, then personalized treatment can be determined, but the decision matrix becomes exceedingly complex making effective decisions difficult even for highly trained medical professionals
Solution Approach 1:
The system implements continuous feedback loops where eye tracking data, user responses, and treatment outcomes are fed back to AI algorithms that automatically adjust treatment parameters. This closed-loop system replaces complex manual decision-making matrices with automated algorithms that process multiple factors simultaneously and generate optimized treatment recommendations, reducing decision complexity while maintaining diagnostic precision
Solution Approach 2:
The patent transforms the complex decision matrix into a set of adjustable display parameters and visual stimulus characteristics that can be directly optimized by AI algorithms. By converting diagnostic considerations into concrete display settings such as regional emphasis ratios, brightness levels, and stimulus timing, the system simplifies the translation from diagnosis to treatment while maintaining precision through parameter-based control
Data Source
AI summary
A display emphasis differential between first and second display regions may be applied. First and second visual regions then receive a visual emphasis differential; alternately, a selective digital image filter is provided acting on specific areas of visual content representing different areas of the retina. Visual emphasis differentials can physiologically bias against progressive myopia. Also, a subject focuses central vision at distance, views first and second targets with peripheral vision, and adjusts distance until their vision fuses a third target. Practicing can physiologically bias against progressive myopia. Further, breaks having adaptive intervals and durations while interrupting graphical output physiologically biases against progressive myopia. Content delivery continues with audio text descriptions in place of graphical output. Also, a large learning model database and generative decision support artificial intelligence cooperate to process large, multi-dimensional data sets informing decision support for choices and parameters regarding diagnosis and treatment of progressive myopia.