Mobile Eye Imaging System with Automatic Alignment
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Solution Overview
Problem
Conventional eye imaging devices require professional operation and alignment, limiting their usability for self-examination and remote monitoring, and they often necessitate regular visits to a doctor's office for capturing high-quality images.
Innovation Solution
A mobile device equipped with a camera, machine learning algorithms, and sensors that allow users to capture and analyze their own eye images, providing real-time feedback for improving image quality and detecting changes without the need for an expert, using features like dual cameras, LED sensors, and automatic alignment procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stationary retinal cameras are used for eye imaging, then high quality images can be obtained, but professional operation and manual alignment are required, reducing ease of operation
Solution Approach 1:
The mobile imaging device enables self-examination by automatically performing alignment and image capture without requiring professional operation. The system includes a processor that automatically processes captured images to determine alignment quality and provides feedback to the user for realignment, allowing the user to independently obtain high-quality eye images.
Solution Approach 2:
The system provides real-time feedback to the user regarding image quality and alignment status. The processor analyzes captured images and communicates alignment quality to the user, enabling iterative self-adjustment to achieve optimal image quality without professional intervention.
2Ease of operation
If hand-held imaging devices are used for retinal screening, then ease of operation is improved, but precise alignment by a second person is still required, increasing device complexity
Solution Approach 1:
The mobile device performs self-alignment by automatically processing captured images to determine alignment quality. The system eliminates the need for a second person to perform alignment by incorporating automated image analysis and user feedback mechanisms that guide the user to achieve proper alignment independently.
Solution Approach 2:
The system replaces manual mechanical alignment performed by a second person with automated electronic image processing and analysis. The processor automatically evaluates alignment quality based on captured images and provides digital feedback, substituting the mechanical alignment process with an automated computational approach.
3Measurement precision
If professional clinicians perform eye examinations, then image quality is ensured, but regular visits to doctor's office are required, increasing loss of time
Solution Approach 1:
The mobile imaging device enables users to perform self-examinations at home, eliminating the need for regular visits to the doctor's office. The automated image capture and analysis functions allow users to independently monitor their eye health over time, significantly reducing time loss associated with travel and scheduling appointments.
Solution Approach 2:
The system enables continuous monitoring of eye health by allowing users to capture and analyze images at any time without interruption. This continuous capability replaces periodic professional examinations with ongoing self-monitoring, maintaining image quality assessment without time loss from regular visits.
4Ease of operation
If automated image quality determination is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The mobile device leverages existing multi-functional components (camera, display, processor) already present in standard mobile devices to perform automated image quality determination. By utilizing the device's inherent capabilities for image capture, processing, and user interaction, the system achieves automation without significantly increasing overall device complexity.
Data Source
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AI summary
A mobile system has a circuitry, which has a camera. The circuitry captures a plurality of images of a user's eye with the camera, determines based on at least one of the captured images of the eye, an image quality of at least one feature of the eye in the captured image, and outputs a user instruction for changing the image quality of the at least one feature of the eye.