Artificial Neural Network Analysis of Flow Cytometry Data for Cancer Diagnosis
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Solution Overview
Problem
Current cancer diagnostic tests lack sensitivity and are prone to false positives and negatives, being invasive, costly, and labor-intensive, making them inefficient for early detection and confirmation.
Innovation Solution
The use of artificial neural networks to analyze flow cytometry data, eliminating the need for manual gating and providing a computationally efficient representation of cell populations, enabling accurate detection of cancer-specific cell populations like MDSCs in peripheral blood samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional flow cytometry data analysis methods (gating) are used, then the analysis process is simple and widely applicable, but the detection accuracy and reliability are insufficient for cancer diagnosis
Solution Approach 1:
The patent replaces the manual gating method with an artificial neural network-based automated analysis system. The neural network processes multidimensional flow cytometry data to automatically identify and classify cell populations, eliminating the need for manual gate definition and improving detection accuracy and reliability for cancer diagnosis.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between the flow cytometer and the analysis results. This intermediary automatically processes the complex multidimensional data, extracting relevant features and making diagnoses without requiring manual intervention, thus improving reliability while managing complexity through automation.
2Productivity
If manual gating methods are used for flow cytometry data analysis, then the process is labor-intensive and subjective, but the method is easier to operate and requires less computational resources
Solution Approach 1:
The patent implements a self-service automated analysis system where the artificial neural network independently processes flow cytometry data without requiring manual gating operations. The system automatically performs data processing, feature extraction, and diagnosis, significantly improving productivity while reducing the operational burden on users.
Solution Approach 2:
The patent substitutes manual gating operations with an automated neural network-based system. This replacement eliminates labor-intensive manual processes and subjective interpretation, improving analysis efficiency and productivity while the system handles complexity internally.
3Reliability
If conventional screening tests are used, then the tests are widely applicable and easy to administer, but they generate numerous false positives and negatives, reducing reliability
Solution Approach 1:
The patent replaces conventional screening tests with an AI-based diagnostic system that uses artificial neural networks to analyze flow cytometry data. This substitution improves diagnostic accuracy and reduces false positives and negatives by leveraging sophisticated pattern recognition and machine learning algorithms.
Solution Approach 2:
The patent changes the analytical parameters from conventional single-parameter or simple multi-parameter tests to sophisticated multidimensional analysis using artificial neural networks. This parameter transformation enables the system to capture complex patterns in flow cytometry data that conventional tests miss, improving reliability and diagnostic accuracy.
4Reliability
If expensive analytical techniques such as DNA or RNA sequencing are used for liquid biopsy, then the detection sensitivity is improved, but the cost and complexity of the test increase
Solution Approach 1:
The patent uses readily available flow cytometry technology and standard fluorescent markers instead of expensive DNA or RNA sequencing techniques. By leveraging existing, cost-effective flow cytometry infrastructure combined with artificial neural network analysis, the system achieves high detection sensitivity without requiring costly advanced analytical techniques.
Solution Approach 2:
The patent substitutes expensive DNA/RNA sequencing with flow cytometry-based cellular analysis enhanced by artificial neural networks. This substitution maintains high detection sensitivity for cancer biomarkers while significantly reducing test complexity and cost by using established flow cytometry technology rather than cutting-edge sequencing platforms.
Data Source
AI summary
The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.