Automated Fundus Drawing Generation via Image Processing
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Solution Overview
Problem
Manual creation of fundus drawings is time-consuming and prone to inconsistencies, with errors and missing labels being difficult to detect, as doctors often keep drawings without the original fundus images.
Innovation Solution
Automated systems and methods for generating fundus drawings through image processing, feature extraction, and machine learning models that analyze co-occurrence to recommend labels, utilizing techniques like SIFT, HOG, deep learning, and co-occurrence analysis to personalize label recommendations based on user history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of fundus drawings is used, then doctors can create customized drawings with their expertise, but the process is time-consuming and prone to inconsistencies
Solution Approach 1:
The system enables self-service by automatically generating fundus drawings without requiring manual intervention from doctors. The automated system extracts features from fundus images and generates standardized drawings consistently, eliminating the time-consuming manual process while maintaining reliability through algorithmic consistency.
Solution Approach 2:
The patent replaces the manual mechanical process of drawing creation with an automated image processing system. Machine learning models and computer vision algorithms substitute the doctor's manual drawing actions, transforming the mechanical act of drawing into an automated computational process that is both faster and more consistent.
2Loss of information
If doctors keep fundus drawings without original fundus images, then storage space is reduced, but errors and missing labels become hard to detect
Solution Approach 1:
The system implements feedback by automatically comparing the generated fundus drawing against the original fundus image. The verification process provides feedback on whether the drawing accurately represents the original image, enabling detection of errors and missing labels while maintaining efficient storage by not requiring permanent retention of original images.
Solution Approach 2:
The patent applies preliminary action by performing verification and validation of fundus drawings before they are finalized and stored. The system checks for errors and completeness during the generation process, catching issues early before the drawing is committed to the medical record, thus eliminating the need to retain original images for error detection.
3Adaptability or versatility
If multiple doctors create fundus drawings manually, then diverse expertise can be applied, but inconsistency across drawings from different doctors increases
Solution Approach 1:
The system achieves universality by creating a standardized fundus drawing generation process that works consistently across different users and cases. The automated system applies the same feature extraction and drawing generation algorithms to all fundus images, ensuring uniformity and consistency while maintaining the ability to handle diverse medical conditions through the same universal process.
Data Source
AI summary
Techniques for automating the generation and analysis of fundus drawings are described. Captured images undergo image processing to extract information about image features. Fundus images are generated and recommended labels for the fundus drawing are generated. Fundus drawings can be analyzed and undergo textual processing to extract existing labels. Machine learning models and co-occurrence analysis can be applied to collections of fundus images and drawings to gather information about commonly associated labels, label locations, and user information. The most frequently used labels associated with the image can be identified to improve recommendations and personalize labels.