Ophthalmologic Tomography Reference Data Control
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Solution Overview
Problem
Current ophthalmologic photographing apparatuses face challenges in capturing sufficient information to observe the progression of lesions in patients, as each patient has unique disease characteristics and fixed photographing patterns are inadequate for comprehensive observation.
Innovation Solution
An ophthalmologic photographing apparatus that includes a photographing optical system with an optical scanner and detector for capturing tomographic images, a reference data setting means to set previous images as reference data, and an image capture data setting means to control the system based on this data for follow-up image acquisition, allowing for patient-specific and efficient lesion progression monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed photographing patterns are used for all patients, then the operation process is simplified and standardized, but insufficient information is obtained to observe lesion progression in individual patients
Solution Approach 1:
The system performs preliminary actions by automatically capturing a series of images at predetermined intervals before the examination. These preliminary images serve as reference data for subsequently determining optimal photographing positions, eliminating the need for manual pattern selection and ensuring comprehensive lesion coverage.
Solution Approach 2:
The system uses feedback mechanisms by comparing automatically captured images with reference images to detect lesion areas and determine optimal photographing positions. This feedback loop ensures that follow-up examinations focus on relevant regions while maintaining standardized operation procedures.
2Loss of information
If manual selection of scan lines and photographing positions is performed, then comprehensive lesion information can be obtained, but time and effort are significantly increased for follow-up examinations
Solution Approach 1:
The system performs self-service by automatically capturing images, determining lesion areas, and selecting optimal photographing positions without requiring manual intervention. The automated processing of comparing images and identifying regions of interest significantly reduces the time and effort needed for follow-up examinations while maintaining comprehensive lesion monitoring.
Solution Approach 2:
The system replaces manual mechanical operations with automated image processing algorithms. Instead of manually selecting scan lines and photographing positions, the system uses computer-based image comparison and analysis to automatically determine optimal examination parameters, thereby reducing time consumption.
3Loss of information
If multiple images are captured at different positions for follow-up, then sufficient information for lesion progression observation is obtained, but the complexity of image capture settings increases
Solution Approach 1:
The system achieves universality by implementing a standardized automated workflow that can be applied to all patients regardless of specific lesion characteristics. The same automated process of capturing reference images, comparing them with follow-up images, and determining photographing positions works universally, simplifying the interface and reducing setting complexity.
Solution Approach 2:
The system segments the image capture process into distinct automated stages: capturing reference images at predetermined intervals, comparing these images to identify lesion areas, and determining optimal photographing positions based on the comparison results. This segmentation allows each stage to be handled automatically without requiring complex overall settings.
4Productivity
If follow-up examinations use predetermined fixed positions, then the examination process is efficient and quick, but the ability to adapt to individual patient disease characteristics is reduced
Solution Approach 1:
The system implements dynamics by making photographing positions adaptive rather than fixed. While the overall process remains efficient and automated, the specific photographing positions are dynamically determined based on individual patient characteristics and lesion locations identified through image comparison, allowing the system to adapt to each patient's unique disease presentation.
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 the acquisition of sufficient information for observing lesion progression on a patient-by-patient basis, reducing time and effort in follow-up image captures while allowing for flexible adjustment of photographing conditions.
Implementation Method 1
an optical scanner configured to scan light emitted from a light source over an examinee's eye
Implementation Method 2
a detector configured to detect a coherent state of reflected light of measurement light from the examinee's eye, the measurement light having been emitted from the light source, and reference light
Data Source
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AI summary
An ophthalmologic photographing apparatus includes: a photographing optical system including an optical scanner for scanning an examinee's eye with measurement light and a detector for detecting a coherent state of reflected light of the measurement light from the examinee's eye and reference light, the photographing optical system being configured to capture a tomographic image of the examinee's eye in response to an output signal from the detector; a reference data setting means for setting a photographing condition of a previously acquired captured image as reference data for a follow-up; an image capture data setting means for setting the reference data set by the reference data setting means as image capture data for the follow-up; and a tomographic image acquisition control means for acquiring a tomographic image of the examinee's eye by controlling the photographing optical system based on the reference data set as the image capture data.