Portable AI Retinal Camera for Offline Disease Diagnosis
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Solution Overview
Problem
Existing fundus cameras lack portability, on-board AI capabilities, and require network connectivity for data analysis, which can interrupt clinical workflows and compromise diagnostic accuracy.
Innovation Solution
A portable medical diagnostics device with integrated AI, featuring a retina camera that includes a housing, display, light source, imaging optics, image detector array, and electronic processing circuitry for real-time disease detection and diagnosis without network connectivity, using techniques such as custom-built shallow neural networks and model optimization to reduce processing power and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fundus cameras are made portable with integrated AI capabilities, then ease of operation and diagnostic accuracy are improved, but device complexity increases
Solution Approach 1:
The patent combines multiple functions (fundus imaging, AI processing, display, and diagnostic capabilities) into a single portable device. The imaging system, machine learning model, and user interface are integrated within one housing, eliminating the need for separate desktop equipment and external computing resources, thereby achieving portability while managing complexity through functional consolidation.
Solution Approach 2:
The patent replaces complex mechanical/desktop-based processing systems with electronic and software-based solutions. Instead of using traditional desktop fundus cameras with external computers for image analysis, the invention uses electronic processing circuitry with integrated machine learning models to perform AI-based diagnostic analysis directly within the portable device.
2Measurement precision
If network connectivity is required for data analysis, then measurement precision may be improved, but productivity and ease of operation deteriorate due to workflow interruptions
Solution Approach 1:
The portable fundus camera performs self-service by containing all necessary processing capabilities within the device itself. The electronic processing circuitry with integrated machine learning models can independently analyze retinal images and provide diagnostic results without requiring external network connectivity or remote server access, enabling autonomous operation in clinical settings.
Solution Approach 2:
The device performs preliminary action by pre-loading machine learning models and processing capabilities into the portable unit before clinical use. This allows the device to immediately process and analyze retinal images upon capture without needing to transfer data over a network, eliminating workflow interruptions and maintaining diagnostic accuracy through pre-configured AI algorithms.
3Measurement precision
If deep learning models are used for disease detection, then measurement precision is improved, but use of energy and processing power requirements increase
Solution Approach 1:
The patent applies parameter changes by adapting deep learning models for deployment on portable hardware with limited processing power and energy resources. This involves optimizing model architecture, reducing computational complexity, and adjusting processing parameters to balance diagnostic accuracy with the energy and computational constraints of battery-powered portable devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, real-time, and portable retinal disease diagnosis with improved usability and security, facilitating better data capture and analysis, and reducing the need for network connectivity.
Implementation Method 1
imaging optics supported by the housing, the imaging optics configured to receive light reflected by the body part
Implementation Method 2
an image detector array configured to receive light from the imaging optics and to sense the received light
Data Source
AI summary
A handheld, portable devices with integrated artificial intelligence (AI) configured to assess a patient's body part to detect a disease and methods of operating such devices are disclosed. In some cases, a device can be a retina camera configured to assess a patient's retina and, by using an on-board AI retinal disease detection system, provide real-time analysis and diagnosis of the patient's retina. Easy and comfortable visualization of the patient's retina can be facilitated using such retina camera, which can be placed over the patient's eye, display the retina image on a high-resolution display, analyze a captured image by the on-board AI system, and provide determination of presence of a disease.