Corneal Astigmatism Vector Planning via Semi-Meridian Segmentation
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Solution Overview
Problem
Current methods for determining and treating astigmatism in the cornea are inadequate, as they fail to accurately represent the semi-meridian parameters and often result in residual irregular astigmatism, leading to suboptimal visual outcomes.
Innovation Solution
A method involving keratometric mapping and vector summation to determine semi-meridian parameters, weighting zones based on proximity and area, and combining topographic and refractive parameters for optimized laser ablation to reduce and regularize astigmatism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keratometric methods are used to determine corneal astigmatism, then the measurement process is simple, but the accuracy and representation of semi-meridian parameters are inadequate
Solution Approach 1:
The cornea is divided into two separate semi-meridians (superior and inferior hemidivisions) for independent analysis. Each semi-meridian is evaluated separately using vector summation of weighted topographic zones, allowing accurate representation of asymmetric corneal astigmatism that conventional single-value methods cannot capture.
Solution Approach 2:
Different weighting coefficients are assigned to different concentric zones (3mm, 5mm, 7mm) based on their proximity to the central axis and area. The central 3mm zone receives higher weight (1.2) as it has greater visual significance, while peripheral zones receive lower weights (1.0 and 0.8), creating a non-uniform quality distribution that reflects optical importance.
2Reliability
If conventional astigmatism treatment methods are used, then the treatment process is straightforward, but residual irregular astigmatism remains leading to suboptimal visual outcomes
Solution Approach 1:
The treatment plan is segmented into separate vector corrections for superior and inferior semi-meridians. Each semi-meridian receives customized ablation based on its specific astigmatism parameters, allowing independent optimization of each hemisphere rather than applying a single symmetric correction.
Solution Approach 2:
The treatment methodology changes from conventional single-parameter keratometry to a multi-parameter vector system that incorporates topographic measurements from multiple zones (3mm, 5mm, 7mm) with different weightings. This parameter transformation enables more precise control of corneal reshaping to eliminate irregular astigmatism.
3Loss of information
If single-zone keratometric measurements are used, then the measurement is quick and simple, but it fails to capture corneal irregularity across different zones
Solution Approach 1:
The corneal topography is segmented into three concentric zones (3mm, 5mm, 7mm) that are measured and analyzed separately. Each zone provides information about local corneal curvature, and the combined weighted vector summation captures both regular and irregular astigmatism components that single-zone measurements miss.
Solution Approach 2:
Measurements from multiple concentric zones are merged through vector summation with appropriate weightings. The combining process integrates information from different corneal regions while accounting for their relative visual importance, producing a comprehensive astigmatism vector that represents the entire corneal surface.
Data Source
AI summary
Techniques are disclosed in which a topographic parameter is determined in each hemidivision of the eye by considering the topography of reflected images from a multiplicity of illuminated concentric rings of the cornea. A simulated spherocylinder is produced to fit into each ring and conform to the topography thereof from which a topographic parameter for each ring can be obtained. All of the topographic parameters of each ring are combined and a mean summated value is obtained representing magnitude and meridian of each hemidivision. From these parameters, a single topographic value for the entire eye (CorT) can be found as well as a value representing topographic disparity (TD) between the two hemidivisions. The topography values for the hemidivisions are used in a vector planning system to obtain treatment parameters in a single step operation.