AI-Driven Ophthalmic Correction Parameter Determination
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Solution Overview
Problem
Current methods for designing and fitting ophthalmic corrections, such as contact lenses and spectacles, face challenges in accurately determining optimal refraction and fit, especially for complex eye conditions like keratoconus, due to reliance on incomplete or variable input data and instrument-dependent measurements.
Innovation Solution
An ophthalmic system that combines AI with a controlled measurement instrument to dynamically measure eye parameters, including wavefront aberrations and cornea shape, and uses machine-learning algorithms trained with trial correction data to determine optimal ophthalmic corrections by correlating measurement data with treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rule-of-thumb systems are used for determining ophthalmic correction parameters, then the process is simple and quick, but the accuracy and reliability of the correction deteriorates, especially for complex eye conditions like keratoconus
Solution Approach 1:
The patent replaces traditional manual trial-and-error fitting methods with an automated AI-based system that uses machine learning algorithms to determine optimal correction parameters. The system substitutes human expert judgment with computational analysis of multiple eye parameters, including corneal topography, wavefront aberrations, and biometric data, to predict the best correction outcome without physical trials.
Solution Approach 2:
The patent introduces an AI algorithm as an intermediary between eye measurement data and correction prescription. This intermediary processes multiple input parameters (corneal curvature, thickness, axial length, wavefront data) and translates them into predicted correction outcomes, serving as a bridge that connects measurement data to treatment decisions while accounting for complex eye conditions.
2Measurement precision
If multiple eye parameters are measured to improve correction accuracy, then the precision of the correction improves, but the complexity of the measurement system and data processing increases
Solution Approach 1:
The patent employs a multi-functional measurement system that captures various eye parameters (corneal topography, wavefront aberrations, biometric data) using integrated instruments. The AI system is designed to process diverse input data types uniformly, making the system versatile and adaptable to different eye conditions while managing complexity through standardized data processing pipelines.
Solution Approach 2:
The patent divides the complex measurement and analysis process into distinct modular components: separate measurement instruments for different parameters, individual data processing modules for each parameter type, and a final integration stage where the AI algorithm synthesizes all inputs. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
3Measurement precision
If AI algorithms are used to determine optimal ophthalmic correction, then the personalization and accuracy of the correction improves, but the quality and representativeness of input data becomes critically important
Solution Approach 1:
The patent performs preliminary data quality assessment and validation before feeding inputs to the AI algorithm. The system pre-processes measurement data to ensure completeness, consistency, and representativeness, flagging or re-acquiring data that does not meet quality thresholds. This preliminary action ensures that the AI receives high-quality input data, maintaining reliability while enabling personalized corrections.
4Measurement precision
If trial corrections are tested and evaluated to improve accuracy, then the precision of the final correction improves, but the time and number of iterations required increases
Solution Approach 1:
The AI algorithm performs virtual trial corrections by predicting outcomes for multiple potential correction parameters before any physical fitting occurs. The system simulates various lens designs and parameters computationally, identifying the most promising candidates in advance, thereby reducing the number of actual physical trials needed and minimizing patient time commitment.
Solution Approach 2:
The patent implements a feedback mechanism where outcomes from trial corrections (whether successful or unsuccessful) are fed back into the AI algorithm to refine future predictions. The system learns from each trial, adjusting its parameter selection for subsequent corrections, which progressively improves accuracy while reducing the number of trials needed over time.
Data Source
AI summary
A method for optimizing an ophthalmic treatment, comprising: measuring a patient's eye with an ophthalmic measurement instrument, fabricating a trial correction lens and testing it on the patient's eye, determining a score or success criteria for the trial correction, using the score or success criteria to provide training information to a machine-learning algorithm, and using the machine-learning algorithm to determine an optimal ophthalmic correction.