Machine-Learning Future Visual Acuity Prediction from Segmented Retinal Images
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Solution Overview
Problem
Conventional machine-learning models are inadequate in accurately and quickly predicting future visual acuity of subjects with eye-related diseases, leading to delayed and ineffective treatment selection.
Innovation Solution
A computer-implemented method using segment-processing and metric-processing machine-learning models to detect retina-related segments and generate segment-specific metrics, which are then processed to predict future visual acuity, incorporating deep-convolutional neural networks and gradient-boosting machines for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine-learning models are used to predict future visual acuity, then the prediction process can be performed, but the accuracy and timeliness of the prediction are insufficient
Solution Approach 1:
The patent segments the retinal image into multiple distinct regions (macula, fovea, optic disc, blood vessels, and background) using a segmentation network. This segmentation allows the system to extract and analyze specific morphological features from each region independently, improving prediction accuracy by focusing on clinically relevant areas while maintaining computational efficiency through parallel processing of segmented regions.
Solution Approach 2:
The patent transforms 2D retinal images into 3D feature representations by extracting morphological features (area, perimeter, circularity, compactness) from segmented regions and combining them with temporal dimensions (baseline and follow-up time points). This dimensional transformation enables the system to capture dynamic changes in retinal structure over time, improving both prediction accuracy and timeliness by analyzing structural evolution rather than static images.
2Reliability
If treatment selection is delayed until efficacy is confirmed, then treatment effectiveness can be verified, but the window for early intervention is lost
Solution Approach 1:
The patent performs preliminary prediction of future visual acuity using machine-learning models before treatment initiation. By analyzing baseline retinal images and extracting morphological features from segmented regions, the system predicts likely treatment outcomes in advance. This preliminary action enables clinicians to select treatments based on predicted efficacy rather than waiting for actual treatment results, thereby maintaining the window for early intervention while still verifying effectiveness through follow-up imaging.
Solution Approach 2:
The patent incorporates feedback loops where follow-up retinal images are compared against baseline images and treatment responses are monitored. The system uses this feedback to verify predicted treatment efficacy and adjust future predictions. This feedback mechanism ensures that treatment selection is both timely (based on preliminary predictions) and reliable (verified through ongoing monitoring of treatment response and visual acuity changes).
Data Source
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AI summary
Methods and systems disclosed herein relate generally to systems and methods for predicting a future visual acuity of a subject by using machine-learning models. An image of at least part of a retina of a subject can be processed by one or more first machine-learning models to detect a set of retina-related segments. Segment-specific metrics that characterize a retina-related segment of the set of retina-related segments can be generated. The segment-specific metrics can be processed by using a second machine-learning model to generate a result corresponding to a prediction corresponding to a future visual acuity of the subject.