Ophthalmic Image Routing for Device-Specific AI Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ophthalmological information processing systems fail to consider selecting an appropriate server for image analysis based on the device that captures the image or the entity requesting the analysis.
Innovation Solution
An information processing system that includes an image acquisition device and a first information processing device, which identifies and transmits data to an appropriate image diagnosis device based on additional information, such as device specifications and user information, to perform specific image diagnoses using artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single ophthalmological information processing server is used for all image analysis, then system complexity is reduced, but diagnostic accuracy and suitability for specific imaging devices deteriorate
Solution Approach 1:
The patent segments the ophthalmological information processing function into multiple specialized servers, each equipped with AI models trained for specific imaging devices or diagnostic tasks. Instead of one general-purpose server, the system divides processing capabilities into dedicated units that can be selectively invoked based on the imaging device used and the type of diagnosis required, thereby maintaining diagnostic accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent implements a universal server selection mechanism that can route images from various imaging devices to appropriate specialized servers. The system maintains a registry of multiple servers with different expertise areas, and a central coordination mechanism that universally handles routing decisions based on device type, image characteristics, and diagnostic requirements, enabling one system to serve multiple specialized functions.
2Manufacturing precision
If multiple specialized servers are used for different image diagnoses, then diagnostic accuracy improves, but system complexity and device selection difficulty increase
Solution Approach 1:
The patent introduces an intermediary component that acts as a mediator between imaging devices and multiple specialized servers. This intermediary receives images from various devices, analyzes their characteristics, and automatically routes them to the most appropriate specialized server based on pre-defined criteria and AI model capabilities. This intermediary layer shields users from the complexity of multiple servers while ensuring accurate routing for optimal diagnostic results.
Solution Approach 2:
The patent utilizes parameter-based routing where the system evaluates multiple parameters including imaging device type, image characteristics, diagnostic task requirements, and server capabilities. By dynamically changing routing parameters based on these inputs, the system automatically selects the most suitable specialized server without requiring manual intervention, thereby managing complexity through automated parameter-driven decision-making.
3Adaptability or versatility
If manual selection of image processing server is required, then system flexibility increases, but operational efficiency and time consumption deteriorate
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically performs server selection without requiring manual user input. The intermediary component autonomously evaluates image characteristics, imaging device information, and diagnostic requirements, then independently selects and routes images to the appropriate specialized server. This self-service capability maintains system flexibility by adapting to different scenarios while dramatically improving operational efficiency by eliminating manual selection steps.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors routing decisions, diagnostic outcomes, and system performance. Based on this feedback, the intermediary component refines its server selection algorithm, learning from past decisions and improving routing accuracy over time. This feedback-driven approach enables the system to automatically adapt to new imaging devices and diagnostic requirements while maintaining high operational efficiency through increasingly accurate automated selection.
Data Source
AI summary
An information processing system comprises: an image acquisition device acquiring subject eye image data of a patient; and a first information processing device communicating with the image acquisition device and storing the image data, the image acquisition device transmits, to the first information processing device, the image data and first transmission data including additional information used to identify an image diagnosis device performing image diagnosis on the image data, the first information processing device: stores the image data when receiving the first transmission data from the image acquisition device; identifies, based on the additional information, a first image diagnosis device performing a first image diagnosis on the image data and/or a second image diagnosis device performing a second image diagnosis differing from the first image diagnosis on the image data; and transmits second transmission data including the image data to the identified image diagnosis device.