IOL Selection via Distribution Functions
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Solution Overview
Problem
Current methods for selecting intraocular lenses (IOLs) and surgical parameters in ophthalmology primarily rely on individual parameters or mean values from patient groups, failing to account for the specific characteristics and dependencies of individual patients and treatment conditions, leading to systematic and statistical errors.
Innovation Solution
A method and system that utilize input parameters with distribution functions to optimize the selection of IOLs and surgical parameters, incorporating biometric data, patient-specific information, and surgical parameters, with a graphical user interface to provide decision aids based on optimized distribution functions for improved refractive outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual parameters or mean values from patient groups are used for IOL selection, then the selection process is simple, but systematic and statistical errors occur leading to reduced accuracy
Solution Approach 1:
The patent transforms the selection approach from using single parameter values to using distribution functions that characterize entire parameter sets. This allows the system to account for statistical variations and dependencies among multiple parameters simultaneously, resolving the contradiction by maintaining computational feasibility while significantly improving accuracy through probabilistic modeling of biometric data, IOL parameters, and surgical factors.
Solution Approach 2:
The patent creates a composite model that integrates multiple input parameters (biometric data, IOL characteristics, surgical parameters) into a unified distribution function framework. This composite approach combines various data sources and their interdependencies into a single probabilistic model, enabling accurate predictions of refractive outcomes while accounting for the complexity of real-world variations in patient anatomy and surgical conditions.
2Measurement precision
If distribution functions with multiple varied input parameters are used, then accuracy and reliability of refractive outcomes improve, but the calculation and selection process becomes more complex
Solution Approach 1:
The patent replaces complex manual calculation and iterative optimization processes with an automated computer-based system. The calculation model automatically processes distribution functions, performs Monte Carlo simulations or other probabilistic analyses, and generates optimized IOL selections without requiring manual intervention. This substitution of automated computational mechanics resolves the contradiction by handling the mathematical complexity while providing user-friendly interfaces and automated decision support.
Solution Approach 2:
The patent uses Monte Carlo simulation to create virtual copies of the surgical scenario by generating numerous randomized parameter sets based on the input distribution functions. Instead of directly calculating the complex interactions of all parameters, the system creates simulated patient cases and surgical outcomes, then analyzes these copies to determine the optimal IOL selection. This copying approach simplifies the analysis of complex probabilistic systems while maintaining accuracy.
3Ease of operation
If only mean values from patient groups are considered, then the selection method is straightforward, but patient-specific characteristics and treatment dependencies are not accounted for
Solution Approach 1:
The patent applies local quality by allowing different portions of the parameter space to have different distribution characteristics tailored to specific patient subgroups. The system can define different distribution functions for different patient categories (e.g., based on age, anatomy, or surgical history) while maintaining a unified framework. This enables patient-specific customization without abandoning the efficiency of group-based approaches, as the same methodology adapts locally to individual patient characteristics.
Solution Approach 2:
The patent transforms the static mean-value approach into a dynamic system where distribution functions can be adjusted and optimized based on patient-specific data. The system allows real-time modification of input parameters and their distributions during the selection process, enabling the methodology to adapt to individual patient characteristics while maintaining computational efficiency through structured probabilistic modeling and automated optimization algorithms.
Data Source
AI summary
Selection of an appropriate intraocular lens (IOL) and/or the applicable surgical parameters for optimizing the results of refractive procedures on the eye. Features of the IOL are crucial for the selection and/or adjustment of the optimal IOL, but so is the IOL selection method (and parameters) from a surgical perspective. For the method, corresponding output parameters are determined from predetermined, estimated, or measured input parameters and/or their mean values, wherein at least two input parameters are varied with one another and which have at least one input parameter as a distribution function. The resulting function is optimized by means of corresponding target values and the determined distribution function of one or more output parameters is used as a decision aid. The present solution is used for selecting an appropriate IOL and/or the applicable surgical parameters and is applicable in the field of eye surgery for implanting IOLs.