Nomogram Computation System for Refractive Laser Surgery
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Solution Overview
Problem
Conventional refractive laser surgery systems often result in sub-optimal outcomes due to the failure to account for noise in data when calculating nomograms, leading to overly aggressive corrections.
Innovation Solution
A method and system that calculate a statistically based offset from a database of treatment outcomes to adjust correction prescriptions, using a confidence interval to optimize the nomogram and reduce the likelihood of aggressive adjustments, incorporating a processor and software to perform least-squares or minimum least-squares error fits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a standard least-squares fit is used to compute nomograms from treatment outcome data, then the nomogram can be calculated efficiently, but the results become overly aggressive and sub-optimal due to not accounting for noise in the data
Solution Approach 1:
The patent changes the computational parameters by introducing confidence interval calculations and statistical significance testing into the nomogram computation process. Instead of using a simple least-squares fit, the system calculates confidence intervals for each data point and uses these to weight the influence of different data points on the final nomogram parameters, thereby reducing the impact of noisy data while maintaining computational efficiency
Solution Approach 2:
The system implements feedback by using treatment outcome data from previously treated patients to continuously refine and update the nomogram. The confidence interval analysis provides feedback on the reliability of each data point, allowing the system to adjust the nomogram parameters based on the statistical quality of the underlying data, thus improving precision over time
2Manufacturing precision
If empirical nomogram adjustments are made based on prior experience and demographic data, then outcomes can be improved for specific patient populations, but the system lacks objectivity and consistency across different clinicians
Solution Approach 1:
The patent creates a universal nomogram computation system that can be applied across all clinicians and patient populations. The statistical methods used to calculate confidence intervals and determine significant trends provide an objective framework that works universally, eliminating clinician-specific biases while still allowing for population-specific adjustments when the data supports them
Solution Approach 2:
The system enables self-service by automatically analyzing treatment outcome data and generating updated nomograms without requiring manual empirical adjustments by individual clinicians. The statistical algorithms automatically identify trends and calculate appropriate corrections, providing consistent, objective nomogram updates that serve all clinicians equally
Data Source
AI summary
A method for optimizing a prescription for laser-ablation corneal treatment includes receiving a measured correction prescription for a current patient. Next a database of treatment outcomes on a plurality of previously treated patients is accessed. The database contains a desired correction, and an actual correction. A difference between the desired correction and the actual correction represents an over- or undercorrection resulting from surgery. From the difference data is calculated a distribution of data points as a function of correction level. From the data-point distribution is calculated a statistically based offset applicable to the correction prescription for matching actual corrections with desired corrections. From the data-point distribution is calculated a confidence interval of the data using a predetermined confidence level. The statistically based offset is then adjusted based upon the confidence interval to provide an optimized prescription. The adjusted offset is then output for use in performing a refractive procedure.