LASIK Flap Aberration Compensation via Deconvolution
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Solution Overview
Problem
LASIK refractive surgery induces changes in corneal refraction and high-order aberrations, such as spherical aberration, coma, and trefoil, which affect treatment outcomes and are not adequately addressed by existing methods.
Innovation Solution
The use of deconvolution techniques and statistical analysis to estimate and adjust for flap-induced aberrations based on site-specific, surgeon-specific, and tool-specific parameters, allowing for customized treatment planning and compensation during LASIK procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deconvolution techniques and statistical analysis are used to estimate and adjust for flap-induced aberrations, then treatment precision is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary statistical analysis and deconvolution techniques during the treatment planning phase to estimate flap-induced aberrations before the actual LASIK procedure. By pre-calculating compensation values based on site-specific, surgeon-specific, and tool-specific parameters, the system reduces the need for complex real-time processing during surgery, thus improving treatment precision while managing device complexity.
Solution Approach 2:
The system creates a computational model (copy) of the flap creation process and its induced aberrations using statistical data from multiple sources. This virtual model allows the system to predict and compensate for aberrations without requiring complex physical measurement devices during surgery, thereby improving treatment precision while minimizing additional hardware complexity.
2Adaptability or versatility
If site-specific, surgeon-specific, and tool-specific parameters are collected and analyzed, then treatment customization is improved, but data collection requirements and processing time increase
Solution Approach 1:
The system merges multiple data sources including site-specific statistical analyses, surgeon-specific historical data, and tool-specific parameters into a unified computational model. By integrating these diverse parameters into a single comprehensive framework, the system achieves high treatment customization without requiring separate processing for each parameter type, thus improving adaptability while reducing overall processing time.
Solution Approach 2:
The system transforms raw clinical data into standardized parameters that can be efficiently processed by the deconvolution algorithm. By converting diverse input data (site statistics, surgeon techniques, tool specifications) into a common parameter format, the system enables comprehensive treatment customization while minimizing the time required for data collection and processing.
3Reliability
If flap-induced aberrations are compensated for through adjusted treatment planning, then post-operative visual quality is improved, but treatment planning complexity increases
Solution Approach 1:
The system incorporates feedback from statistical analysis of post-operative outcomes from previous procedures to continuously refine the deconvolution model. By using historical data on actual flap-induced aberrations and their compensation effectiveness, the system automatically adjusts treatment planning parameters, improving post-operative visual quality while reducing the need for manual intervention and simplifying the overall planning process.
Data Source
AI summary
Embodiments of the present invention encompass systems and methods for customized vision treatments that account for effects associated with corneal flap creation.