Fundus Autofluorescence Image Grading for Reliable Quality Selection
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Solution Overview
Problem
The manual interpretation of fundus autofluorescence (FAF) images for clinical diagnosis is time-consuming, resource-intensive, prone to human error, and not scalable for real-world medical practice, affecting the accuracy of disease diagnosis and patient management.
Innovation Solution
A two-step approach using a gradeability model and a quality model, both based on deep learning and machine learning, to automate the analysis of FAF images, determining their gradability and quality score, respectively, thereby standardizing and improving the efficiency of image interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual interpretation of FAF images is used, then diagnostic accuracy can be maintained through expert judgement, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of expert grader review with an automated machine learning system. The ML model processes FAF images to determine gradeability status and quality scores, eliminating the need for time-consuming manual interpretation while maintaining consistent diagnostic criteria application.
Solution Approach 2:
The system enables self-service by allowing the FAF image analysis process to evaluate itself through automated quality assessment. The machine learning model independently determines whether images are gradable and assigns quality scores without requiring external expert intervention, making the process autonomous and scalable.
2Adaptability or versatility
If manual interpretation by specialized graders is used, then subjective judgement can be applied, but human error and bias increase
Solution Approach 1:
The patent transforms the subjective parameter of expert judgement into objective quantifiable parameters through machine learning. The system converts qualitative assessments into measurable quality scores and binary gradeability classifications, eliminating human bias while maintaining the ability to adapt to different diagnostic criteria through model training.
Solution Approach 2:
The system creates a digital copy of the expert grading process through machine learning models trained on expert-annotated data. This digital replica captures and standardizes diagnostic criteria application across multiple images without the variability inherent in human judgement, ensuring consistent and reproducible results.
3Productivity
If automated systems are implemented, then processing speed and scalability improve, but system complexity increases
Solution Approach 1:
The patent segments the automated analysis system into distinct functional modules: an image processing system that handles FAF image input, a machine learning model that performs classification and scoring, and an output generation component that provides quality assessments. This modular architecture manages system complexity while enabling high processing speed and scalability.
4Measurement precision
If manual quality assessment is performed, then detailed evaluation can be conducted, but resource intensity increases
Solution Approach 1:
The patent replaces resource-intensive manual quality assessment with an automated machine learning system that provides detailed quality metrics. The ML model evaluates multiple image quality parameters simultaneously, delivering comprehensive assessments without the proportional increase in human resources that manual methods would require.
Data Source
AI summary
Systems and methods herein provide analysis of fundus autofluorescence (FAF) images. A set of FAF images are received, each FAF image in the set of FAF images containing at least a portion of an eye of a patient. Each FAF image in the set of FAF images is processed by generating a set of features based on the FAF image, applying the set of features to a deep learning model to determine a gradeability status of the FAF image, and responsive to determining the FAF image is gradable, applying the set of features to a machine-learned model to determine a quality score for the FAF image. Responsive to processing each FAF image in the set of FAF images, a highest quality gradable FAF image of the patient is determined, where the highest quality gradable FAF image is the FAF image having the highest quality score.