Retinal Image AI Detection for Privacy-Aware Point-of-Care Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The burden of visual impairment remains high globally due to retinal diseases, with many cases being potentially avoidable and imposing significant personal and financial burdens, and existing AI implementations face challenges in data sharing, privacy, regulatory issues, and lack of computational resources in low-resource settings.
Innovation Solution
A machine-learning-based system using convolutional neural networks and transfer learning to analyze ophthalmic images, particularly retinal images, for early detection of diseases like AMD, DR, glaucoma, and RVO, leveraging non-domain images for training and deploying on mobile platforms for point-of-care diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI implementations are deployed for disease detection, then detection accuracy is improved, but data sharing and privacy issues worsen
Solution Approach 1:
The patent introduces federated learning as an intermediary mechanism that enables multiple institutions to collaboratively train AI models without directly sharing sensitive patient data. The system uses secure enclaves and encrypted communication channels as intermediaries to exchange only model updates rather than raw data, thus maintaining detection accuracy while preserving data privacy and enabling data sharing across institutions.
2Measurement precision
If advanced AI models are used for comprehensive disease detection, then detection capability is improved, but computational resource requirements worsen
Solution Approach 1:
The patent segments the AI model into multiple specialized sub-models, each trained to detect specific disease conditions (e.g., diabetic retinopathy, glaucoma, AMD). This segmentation allows the system to deploy only the necessary sub-models based on the specific clinical context, reducing overall computational resource consumption while maintaining comprehensive detection capability across different disease types.
Solution Approach 2:
The system implements a hierarchical screening approach where a lightweight preliminary model performs initial screening to identify cases requiring full analysis. This partial action strategy processes only a subset of images with the computationally intensive full model, reducing overall computational resource requirements while maintaining high detection accuracy for critical cases.
3Measurement precision
If specialized domain datasets are used for training, then classification accuracy is improved, but data availability worsens
Solution Approach 1:
The patent develops a universal framework that can process and utilize diverse imaging modalities (fundus photography, OCT, visual field tests) and data types (structural, functional, genetic markers) through a common architecture. This multi-functionality allows the system to leverage data from multiple sources and institutions with different equipment and protocols, increasing training data availability while maintaining classification accuracy through modality-specific preprocessing layers.
Data Source
AI summary
Disclosed herein are systems, methods, devices, and media for carrying out detection of ophthalmic and systemic diseases and disorders. Deep learning algorithms enable the automated analysis of ophthalmic images such as retinal scans to generate accurate detection of various diseases and disorders. Point-of-care implementations allow for rapid and efficient detection outside of the clinical setting.