Customized Intraocular Lens Power Calculation via Ray Tracing
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Solution Overview
Problem
Current regression-based methods for calculating intraocular lens (IOL) power, such as SRK, SRK II, and SRK/T, fail to provide accurate predictions for eyes outside the normal range, particularly for those that have undergone ablative keratorefractive surgery, due to their reliance on historical clinical data and neglect of optical aberrations.
Innovation Solution
A system that measures anterior and posterior corneal topography, axial length, and anterior chamber depth, and uses monochromatic or polychromatic ray tracing to simulate the eye with an IOL implanted, calculating a modulation transfer function (MTF)-based value to select the IOL with the highest MTF value for optimal implantation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If regression-based formulas (SRK, SRK II, SRK/T) are used for IOL power calculation, then the calculation process is simple and quick, but accuracy deteriorates for eyes outside the normal range and for patients who have undergone ablative keratorefractive surgery
Solution Approach 1:
The patent replaces regression-based empirical formulas with ray tracing-based optical modeling. Instead of using statistical correlations derived from historical data, the system uses physics-based optical calculations that trace light rays through the eye's optical components, providing accurate predictions for both normal and abnormal eye geometries including post-refractive surgery eyes.
Solution Approach 2:
The patent changes the fundamental parameters used for IOL power calculation from simple axial length and corneal curvature measurements to comprehensive optical parameters including higher-order aberrations, detailed corneal topography, and ray tracing-based modulation transfer function (MTF) calculations. This parameter transformation enables accurate prediction for eyes with abnormal geometries.
2Ease of operation
If regression formulas derived from historical clinical data are used, then the method is easy to implement, but it fails to account for individual eye characteristics and optical aberrations
Solution Approach 1:
The patent applies local quality by customizing the optical model for each individual patient's eye. Instead of using a universal regression formula, the system incorporates patient-specific parameters including individual corneal topography maps, axial length, anterior chamber depth, and higher-order aberrations to create a personalized optical model that accurately reflects that specific eye's characteristics.
Solution Approach 2:
The patent performs preliminary comprehensive measurements and modeling before IOL selection. The system pre-calculates ray tracing paths, MTF values, and optical quality metrics for various IOL options based on the patient's specific eye parameters, enabling informed decision-making before the actual implantation procedure.
3Device complexity
If standard regression methods are used for post-refractive surgery eyes, then the calculation remains straightforward, but accuracy deteriorates due to altered corneal geometry and aberrations
Solution Approach 1:
The patent creates an accurate optical copy or model of the patient's post-refractive surgery eye using ray tracing. Instead of attempting to correct for surgical effects through empirical adjustments, the system directly models the actual optical path of light through the altered corneal geometry, capturing the true optical behavior including induced aberrations and enabling precise IOL power prediction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides improved accuracy in predicting optimal IOL power for both normal and non-normal eyes, including those with significant aberrations, by considering individual eye characteristics and optical quality metrics, leading to better emmetropic vision outcomes.
Implementation Method 1
For each of a plurality of intraocular lenses (IOLs), simulating the subject eye with the intraocular lens (IOL) implanted in accordance with the measuring, performing either monochromatic or polychromatic ray tracing through the surfaces defining the built eye model
Implementation Method 2
Once that position is determined, the preferred power for an IOL to be implanted is calculated by simple paraxial optics, taking into account that the eye can be modeled under this approximation as a two lens system (cornea + IOL) focusing an image on the retina
Data Source
AI summary
Selecting an optimal intraocular lens (IOL) from a plurality of lOLs for implanting in a subject eye, including measuring anterior corneal topography (ACT), axial length (AXL), and anterior chamber depth (ACD) of a subject eye; selecting a default equivalent refractive index depending on preoperative patient's stage or calculating a personalized value or introducing a complete topographic representation if posterior corneal data are available; creating a customized model of the subject eye with each of a plurality of identified intraocular lenses (IOL) implanted, performing a ray tracing through that model eye; calculating from the ray tracing a RpMTF or RMTF value; and selecting the IOL corresponding to the highest RpMTF or RMTF value for implanting in the subject eye.