AI Single-Cell Tumor Analysis for Phenotype Heterogeneity Classification
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Solution Overview
Problem
Current cancer treatment methods are limited by the inability to accurately classify gene phenotypes of tumors based on a single cell unit, leading to challenges in selecting appropriate treatments and drugs for cancer cells with different characteristics.
Innovation Solution
A single cell analysis device using artificial intelligence to analyze tumor samples at the single cell level, including data pre-processing, cell type classification, phenotype prediction, and heterogeneity materialization to provide personalized treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tumor classification is made based on the sum of all genes from cancer cells, immune cells and stromal cells, then the analysis covers the entire tumor composition, but the classification precision for individual cancer cell phenotypes deteriorates
Solution Approach 1:
The patent segments the tumor sample into individual single cells and analyzes each cell's gene expression profile separately. This segmentation allows precise classification of individual cancer cell phenotypes without the averaging effect of bulk analysis, directly resolving the contradiction between classification precision and analysis complexity by enabling cell-type-specific analysis while maintaining manageable computational complexity through modular processing
2Measurement precision
If single cell analysis is performed to identify different phenotypes, then the classification precision improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex tumor analysis into distinct modules: single cell isolation, RNA extraction, sequencing, and computational analysis. Each module handles a specific aspect of the process, reducing the overall computational complexity while maintaining high phenotype classification accuracy through specialized processing at each stage
Solution Approach 2:
The patent introduces intermediate processing steps including RNA extraction and sequencing as mediators between sample collection and final analysis. These intermediaries transform the complex biological sample into standardized data formats that are more manageable for computational analysis, reducing the direct complexity burden
3Ease of operation
If bulk tumor analysis is used, then the analysis process is simpler, but the ability to detect tumor heterogeneity and select appropriate treatments deteriorates
Solution Approach 1:
The patent segments the tumor analysis to detect heterogeneity by analyzing individual cells rather than bulk tissue. This segmentation reveals diverse phenotypes within the tumor that bulk analysis misses, improving treatment selection reliability while maintaining operational simplicity through automated single-cell processing workflows
Solution Approach 2:
The patent replaces complex manual analysis methods with automated artificial intelligence algorithms that process single cell data. This substitution maintains ease of operation by automating the complex tasks while significantly improving treatment selection reliability through accurate detection of tumor heterogeneity and phenotypic classification
Data Source
AI summary
A device and method are disclosed for performing single-cell tumor analysis using artificial intelligence. Single-cell RNA sequencing data is obtained from a tumor sample of a patient, and pre-processed to remove low-quality or redundant data. Cell types are classified based on gene expression, and tumor subtypes and grades are predicted using trained machine learning models. The system also detects phenotypic heterogeneity within the tumor and generates personalized analysis reports that may include prognosis, metastasis likelihood, and treatment recommendations. This approach enables precision oncology at single-cell resolution through automated data-driven analysis.