AI Vascular Pattern Analysis for Non-Invasive Disease Detection
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Solution Overview
Problem
Current methods for detecting diseases such as cancer and stroke are invasive, time-consuming, and often inaccurate, failing to detect early stages effectively due to limitations in sensitivity and specificity, particularly in analyzing vascular structures.
Innovation Solution
A method utilizing artificial intelligence algorithms to analyze vascular structure patterns by extracting and processing vessel measurements from medical images, applying machine learning techniques to determine disease presence, predict disease onset, and monitor disease progression, incorporating fractal analysis and scaling exponents to quantify vascular physiology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current cancer screening tests are used, then disease detection is performed, but the tests are invasive, time-consuming, and sometimes inaccurate
Solution Approach 1:
The patent replaces mechanical/invasive biopsy procedures with computational image analysis of non-invasive imaging data (CT, MRI, ultrasound). AI algorithms process vascular structure images to detect cancer, eliminating the need for physical tissue sampling while maintaining or improving detection accuracy.
Solution Approach 2:
The patent introduces vascular structure imaging as an intermediary between non-invasive observation and cancer detection. By analyzing the vascular network surrounding tumors through imaging modalities, the system indirectly detects cancer presence without direct tissue contact, reducing invasiveness while preserving diagnostic reliability.
2Measurement precision
If current screening tests are used, then disease detection is performed, but early stages are not detected effectively
Solution Approach 1:
The patent focuses analysis on specific local features of vascular structures - examining detailed characteristics of blood vessel networks, their branching patterns, and morphological properties. This localized analysis of vascular features enables detection of subtle early-stage changes that global or less detailed methods would miss, improving both sensitivity and accuracy.
Solution Approach 2:
The patent transitions from analyzing traditional tumor-centric features to examining vascular network dimensions - the three-dimensional structure, density, and topology of blood vessels surrounding tumors. This dimensional shift to vascular architecture provides new detection capabilities for early-stage cancer that conventional imaging methods cannot detect.
3Productivity
If machine learning algorithms are applied to detect cancer from images, then detection capability is improved, but sensitivity for early stages remains insufficient
Solution Approach 1:
The patent changes the parameters being analyzed by machine learning algorithms - shifting from conventional tumor size, shape, and density parameters to vascular network parameters such as vessel density, branching complexity, and spatial distribution. This parameter transformation enables the AI system to detect early-stage cancer through subtle vascular changes that precede visible tumor growth.
Solution Approach 2:
The patent performs preliminary analysis of vascular structures before tumors become clinically apparent. By examining and characterizing the vascular network in advance, the system establishes a baseline that enables early detection of cancer-induced vascular changes, allowing intervention before tumors reach detectable sizes through conventional methods.
Data Source
AI summary
Methods, computer programs, and systems for detecting disease in vasculature. The method includes obtaining images of the vasculature. The method includes extracting vessel measurements from the obtained images. The method includes determining features of the vasculature in the obtained images based on the extracted vessel measurements. The method includes applying artificial intelligence algorithms to determine if the disease is present in the vasculature based on these vascular features.