Intraocular Lens Selection Using 3D Imaging and ELP Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional one-dimensional measurements for intraocular lens (IOL) power calculation lead to inaccuracies in estimating the effective lens position (ELP), resulting in suboptimal vision outcomes for patients undergoing cataract surgery.
Innovation Solution
Utilizing multi-dimensional images of the eye and machine learning techniques to estimate post-operative anterior chamber depth (ACDpost) and IOL power, incorporating measurements such as angle-to-angle width, pupillary axis, and lens equator position, to improve IOL selection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional one-dimensional measurements are used for IOL power calculation, then the measurement process is simple and quick, but the measurement precision and reliability of ELP estimation deteriorate
Solution Approach 1:
The patent transitions from traditional one-dimensional optical axis measurements to multi-dimensional imaging (anterior segment OCT, Scheimpflug imaging, or ultrasound biomicroscopy) to capture comprehensive anatomical parameters including angle-to-angle width, pupillary axis, and lens equator position, thereby improving ELP estimation accuracy through enhanced measurement dimensions
2Reliability
If multi-dimensional images and machine learning are used to estimate post-operative ACD and IOL power, then the IOL selection accuracy is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs pre-operative multi-dimensional imaging and extraction of anatomical parameters before surgery to estimate post-operative ACD and IOL power using machine learning models, allowing accurate IOL selection to be made in advance based on predicted outcomes rather than relying on traditional formulas
Solution Approach 2:
A prediction engine acts as an intermediary between the multi-dimensional imaging data and the final IOL selection, using machine learning algorithms to process complex anatomical measurements and generate accurate predictions of post-operative ACD and optimal IOL power
Data Source
AI summary
Systems and methods for intraocular lens selection include receiving, by one or more computing devices implementing a prediction engine, pre-operative multi-dimensional images of an eye; extracting, by the prediction engine, pre-operative measurements of the eye based on the pre-operative images; estimating, by the prediction engine using a prediction model based on a machine learning strategy, a post-operative position of an intraocular lens based on the extracted pre-operative measurements; selecting a power of the intraocular lens based on the estimated post-operative position of the intraocular lens; and selecting the intraocular lens based on the selected power. In some embodiments, the systems and methods further include receiving post-operative multi-dimensional images of the eye after implantation of the selected intraocular lens, extracting post-operative measurements of the eye, and updating the prediction model based on the pre-operative measurements and the post-operative measurements.