Intraocular Lens Selection Using Emmetropia Zone Prediction
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Solution Overview
Problem
Traditional one-dimensional measurements for selecting an intraocular lens (IOL) during cataract surgery lead to inaccurate IOL type and power selection, resulting in suboptimal vision outcomes for patients.
Innovation Solution
A system and method using a prediction engine to determine pre-operative measurements, estimate post-operative anterior chamber depth (ACD) and manifest refraction in spherical equivalent (MRSE), and apply emmetropia zone prediction models to select an IOL that optimizes vision outcomes by determining the likelihood of the eye being in the emmetropia zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional one-dimensional measurements are used for IOL selection, then the measurement process is simple and quick, but the accuracy of IOL type and power selection deteriorates, resulting in suboptimal vision outcomes
Solution Approach 1:
The patent transitions from traditional one-dimensional axial length measurements to multi-dimensional measurements including corneal curvature, anterior chamber depth, and other ocular parameters. This dimensional expansion enables more comprehensive eye characterization and improves IOL selection accuracy by capturing the full optical geometry of the eye rather than relying on a single measurement dimension.
Solution Approach 2:
The patent introduces a prediction engine as an intermediary system that processes multiple measurement parameters and delivers personalized IOL selection recommendations. This intermediary layer synthesizes complex multi-dimensional data through algorithms and provides optimized IOL power and type predictions, mediating between raw measurement data and clinical decision-making to improve selection accuracy.
2Reliability
If traditional one-dimensional measurements are used, then the measurement process is straightforward, but the vision outcome optimization deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the prediction engine continuously refines its estimates based on the patient's specific ocular parameters and surgical considerations. The system provides iterative predictions that can be adjusted based on additional factors, creating a feedback loop that improves the reliability of vision outcome predictions while managing system complexity through structured algorithmic approaches.
Solution Approach 2:
The patent employs multiple prediction models that adjust parameters based on different emmetropia zone classifications. The system changes prediction parameters dynamically depending on whether the eye is predicted to fall within or outside the emmetropia zone, allowing optimized vision outcome prediction for different patient scenarios while maintaining manageable system complexity through model selection rather than full parameter complexity.
Data Source
AI summary
Systems and methods for intraocular lens (IOL) selection using emmetropia zone prediction include determining pre-operative measurements of an eye, estimating a post-operative anterior chamber depth (ACD) of an intraocular lens based on the pre-operative measurements, estimating a post-operative manifest refraction in spherical equivalent (MRSE) of the eye with the IOL implanted based on the pre-operative measurements and the estimated post-operative ACD, determining whether the eye with the IOL implanted is likely to be in an emmetropia zone based on the estimated post-operative MRSE, re-estimating the post-operative MRSE of the eye with the IOL implanted using an emmetropia zone prediction model or a non-emmetropia zone prediction model based on the emmetropia zone determining, and providing the re-estimated post-operative MRSE to a user to aid in selection of an IOL for implantation in the eye.