Automated Cytological Analysis Using Neural Network Cell Classification
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Solution Overview
Problem
Current urine cytological analysis is labor-intensive and produces qualitative results, making it inefficient for diagnosing conditions like urothelial carcinoma, which requires automated and quantitative methods for improved accuracy and efficiency.
Innovation Solution
An automated system using an image processing module and neural network to classify cell images from whole slides, providing diagnostic information in a digital format, which can be trained using specialist criteria and improved iteratively, and can be deployed in a cloud-based computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual microscopic examination by technologists and pathologists is used, then diagnostic accuracy can be maintained through expert judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated digital imaging and analysis system. Whole slide images are captured and processed through computer algorithms that automatically identify, segment, and classify cells based on morphological features, eliminating the need for manual microscopic examination while maintaining diagnostic accuracy through quantitative image analysis
Solution Approach 2:
The patent introduces a digital image processing system as an intermediary between the tissue sample and the final diagnosis. The system captures whole slide images, processes them through background deletion, cell identification, and classification algorithms, and generates diagnostic reports, serving as an automated mediator that bridges the gap between raw biological samples and clinical decision-making
2Loss of information
If qualitative results from manual examination are provided, then diagnostic categories can be assigned based on expert interpretation, but the results cannot be directly used to determine intervention without additional clinical context
Solution Approach 1:
The patent transforms qualitative diagnostic categories into quantitative parameters by measuring specific morphological features such as nuclear area, cytoplasmic area, nuclear-cytoplasmic ratio, and cell density. These quantitative measurements provide objective, numerically-based diagnostic information that can be directly integrated with clinical data to guide intervention decisions
Solution Approach 2:
The patent replaces subjective expert interpretation with objective computer-based analysis. The system automatically quantifies cellular features and generates standardized diagnostic reports with numerical data, eliminating the loss of information associated with qualitative assessments and enabling more efficient clinical decision-making through data-driven recommendations
3Ease of manufacture
If traditional cytological methods are used, then cost-effectiveness is maintained compared to surgical pathology, but automation capabilities are limited
Solution Approach 1:
The patent implements full automation of the cytological analysis process by replacing manual microscopic examination with digital whole slide imaging and computer-based image analysis. The system automatically performs background deletion, cell identification, segmentation, and classification, achieving high-level automation while maintaining the cost-effectiveness of cytological methods compared to surgical pathology
Solution Approach 2:
The patent creates digital copies of tissue samples through whole slide imaging, allowing the same physical sample to be analyzed automatically without requiring additional physical processing. This digital replication enables automated analysis while preserving the cost advantages of cytological over surgical methods
Data Source
AI summary
A system and method for delivering diagnostic information to a user in an automated manner. An image processing module reads magnified raw image data from whole slides containing tissue cells. A background deletion process identifies and isolates cell images. A neural network, which is trained based upon image data, classifies the cell images. The classification uses specialist criteria to categorize/segment the cell images into a plurality of discrete cell types. A display process provides the diagnostic information to the user. The neural network can be trained using a plurality of cell types each having the specialist criteria. The diagnostic information includes filtered and/or reorganized images of the tissue cells. Additionally, the user can request the diagnostic information based upon an account that provides payment according to a predetermined formula associated with at least one of a type, format, and timing of information delivered to the user.