Dry Eye Forecast System Using Preliminary Action and Dynamics
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Solution Overview
Problem
Current treatments for dry eye disease lack personalized recommendations for stimulus delivery, failing to effectively address the progressive nature of the condition and its environmental and patient-specific factors.
Innovation Solution
A system that determines environmental and patient-specific properties to form a dry eye forecast, which is used to select a tailored treatment recommendation, including stimulus delivery parameters such as duration and timing, to reduce symptoms and prevent future occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical stimulation is applied to treat dry eye disease, then tear production is increased, but treatment intensity and energy consumption increase
Solution Approach 1:
The system performs preliminary assessment of dry eye severity and environmental conditions before delivering stimulation. By evaluating multiple properties (tear film stability, corneal sensitivity, environmental humidity, temperature) in advance, the system determines the optimal stimulation parameters needed, avoiding unnecessary high-intensity treatment and reducing energy consumption while maintaining effectiveness.
Solution Approach 2:
The stimulation parameters are dynamically adjusted based on real-time assessment of dry eye conditions and environmental factors. The system modifies stimulation intensity, duration, and frequency according to the calculated dry eye forecast, ensuring treatment effectiveness while minimizing energy consumption by applying only the necessary stimulation level.
2Reliability
If personalized treatment recommendations are implemented, then treatment effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the treatment approach into distinct functional modules: environmental sensing, property assessment, dry eye forecasting, and treatment recommendation generation. Each module handles a specific aspect of the personalized treatment process, making the overall complex system manageable and maintainable while delivering comprehensive personalized care.
Solution Approach 2:
The system introduces a computational model (dry eye forecast) as an intermediary that processes multiple input properties and translates them into treatment recommendations. This intermediary layer simplifies the relationship between complex input data and treatment decisions, enabling personalized treatment without requiring direct complex interactions between all system components.
3Use of energy by moving object
If treatment is delayed until symptoms worsen, then treatment intensity can be reduced, but disease progression continues
Solution Approach 1:
The system performs preliminary forecasting of dry eye progression based on current conditions and historical data. By predicting future symptom severity, the system enables proactive treatment initiation before symptoms worsen significantly, allowing for lower intensity treatment that is more effective at preventing progression rather than managing advanced disease.
Solution Approach 2:
The system continuously monitors environmental conditions and patient symptoms, using this feedback to update the dry eye forecast and adjust treatment recommendations. This closed-loop feedback mechanism ensures treatment intensity is optimized based on actual disease progression, preventing both overtreatment and undertreatment while managing disease effectively.
Data Source
AI summary
Generally, a machine may include a processor and a memory connected to the processor, where the memory stores instructions executed by the processor to determine first and second properties related to dry eye symptoms of a patient. The properties may be used to form a dry eye forecast. A treatment recommendation may be selected based at least in part upon the dry eye forecast. The treatment recommendation may be supplied to a device.