Corneal Lesion Analysis Using Anterior Segment CNN
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Solution Overview
Problem
Current methods for diagnosing keratitis, particularly infectious and non-infectious types, are often inaccurate and time-consuming, leading to misdiagnosis and inappropriate treatment, which can result in decreased vision and increased medical expenses.
Innovation Solution
A system and method for analyzing corneal lesions using anterior segment images through machine learning, specifically employing a convolutional neural network (CNN) and a residual network (ResNet) to extract feature information and determine the position and cause of the lesion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specimen cultivation is performed to diagnose keratitis, then diagnostic accuracy is improved, but diagnosis time is significantly increased
Solution Approach 1:
The system performs preliminary analysis of anterior segment images using deep learning models to identify suspicious regions and predict keratitis characteristics before specimen cultivation is completed. This allows clinicians to initiate empirical treatment based on image analysis results while awaiting cultivation confirmation, effectively bridging the time gap between rapid imaging and slow cultivation results.
Solution Approach 2:
The patent introduces an intermediate diagnostic approach using anterior segment images and deep learning analysis as a mediator between rapid clinical observation and slow specimen cultivation. The image analysis system provides preliminary diagnostic information that guides treatment decisions during the waiting period for cultivation results, reducing the overall diagnostic time while maintaining accuracy.
2Productivity
If empirical treatment is provided based on lesion observation, then treatment time is reduced, but diagnostic accuracy deteriorates
Solution Approach 1:
The system provides feedback to clinicians by analyzing anterior segment images and comparing lesion characteristics against a database of known keratitis patterns. The deep learning model generates predictions about the type and severity of keratitis, giving clinicians data-driven feedback to refine their empirical treatment decisions and improve diagnostic accuracy without delaying treatment initiation.
Solution Approach 2:
The system performs preliminary diagnostic analysis of image features before treatment is initiated, extracting relevant characteristics such as lesion shape, size, interface, and position. This preliminary analysis provides clinicians with enhanced diagnostic information upfront, allowing for more accurate empirical treatment decisions while maintaining rapid treatment initiation.
3Measurement precision
If deep learning analysis is applied to anterior segment images, then diagnostic accuracy is improved, but system complexity is increased
Solution Approach 1:
The deep learning system is segmented into distinct functional modules: an image acquisition module, a feature extraction module using convolutional layers, a suspicious region identification module, and a result determination module. This segmentation allows each component to be optimized independently and facilitates integration with existing clinical workflows, reducing the perceived complexity while maintaining high diagnostic accuracy.
4Adaptability or versatility
If machine learning model is trained on clinical information database, then adaptability to changing antibiotic susceptibility is improved, but data processing requirements are increased
Solution Approach 1:
The system performs preliminary extraction of relevant features from clinical information and images before model training. By pre-processing and organizing data in advance, the system reduces the computational burden during model updates and enables more frequent retraining with new clinical data, improving adaptability to changing antibiotic susceptibility patterns while managing data processing requirements.
Data Source
AI summary
A system for and a method of analyzing a corneal lesion using an anterior segment image according to the present invention. The system includes: an image acquisition unit configured to acquire an anterior segment image from the eyeball of a subject, a feature extractor configured to extract feature information on a position and a cause of a lesion in the cornea from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and a result determination unit configured to identify a position of the cornea from anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea.