Machine-Learned IOL Parameter Selection for Atypical Eye Anatomy
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Solution Overview
Problem
Existing methods for determining intraocular lens (IOL) parameters during cataract surgery are inadequate when anatomical parameters deviate from typical ranges, leading to suboptimal surgical outcomes.
Innovation Solution
Utilizing machine learning models trained on historical patient data to generate IOL parameters based on anatomical measurements, including optimal and contraindicated recommendations for cataract surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to determine IOL parameters based on anatomical measurements, then the process is simple and quick, but the accuracy and reliability of surgical outcomes deteriorate when anatomical parameters deviate from typical ranges
Solution Approach 1:
A machine learning model acts as an intermediary between anatomical measurements and IOL parameter selection. The model processes complex relationships between multiple anatomical parameters and historical surgical outcomes, providing optimized IOL recommendations that improve reliability while maintaining ease of use for clinicians.
Solution Approach 2:
The patent replaces traditional manual calculation methods and simplified formulas with an automated machine learning system. This substitution enables the system to analyze complex patterns in historical data and provide more accurate IOL parameter recommendations without requiring clinicians to perform complex manual computations.
2Measurement precision
If machine learning models are used to determine IOL parameters, then the accuracy of surgical outcomes improves, but the complexity of the determination process increases
Solution Approach 1:
The machine learning model is pre-trained on extensive historical surgical data before deployment. This preliminary training action enables the model to learn complex relationships and patterns, allowing it to provide accurate predictions during actual surgical planning without requiring complex real-time computations or manual data processing.
3Reliability
If comprehensive historical data analysis is performed to optimize IOL parameters, then surgical outcomes are improved, but the time required for parameter determination increases
Solution Approach 1:
The machine learning model performs comprehensive data analysis during the offline training phase, processing extensive historical surgical data in advance. This preliminary action enables the model to provide rapid predictions during actual surgical planning, eliminating the need for time-consuming real-time data processing while maintaining high reliability.
Solution Approach 2:
The patent replaces time-consuming manual analysis of historical data with an automated machine learning system. The model rapidly processes anatomical measurements and provides optimized IOL parameter recommendations, significantly reducing the time required compared to traditional manual methods while maintaining or improving outcome reliability.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for performing surgical ophthalmic procedures, such as cataract surgeries. An example method generally includes generating, using one or more measurement devices, one or more data points associated with measurements of one or more anatomical parameters for an eye to be treated. Using one or more trained machine learning models, one or more recommendations are generated including one or more IOL parameters for the IOL to be used in the cataract surgery based, at least in part, on the one or more data points. The machine learning models are trained based on at least one historical data set of data points associated with measurements of anatomical parameters mapped to treatment data and treatment result data associated with each historical patient. The one or more IOL parameters comprise one or more of an IOL type, an IOL power, or IOL placement information for implanting the IOL in the eye.