Machine Learning Tissue Characterization via Fluorescence Clustering
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Solution Overview
Problem
Current methods for assessing blood flow and tissue perfusion in medical imaging are limited by subjective visual evaluation, which can be ambiguous and inconsistent between clinicians, and do not support standardized protocols for comparing perfusion status over time.
Innovation Solution
The use of machine learning algorithms to analyze time series of fluorescence images, categorizing data into clusters based on relevant attributes, and generating spatial maps to characterize tissue and predict clinical data, providing a more accurate and intuitive assessment of blood flow and perfusion patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual evaluation is used to assess blood flow and tissue perfusion, then the assessment process is simple and quick, but the results are subjective and inconsistent between clinicians
Solution Approach 1:
The patent replaces the mechanical/visual assessment system with an automated image processing and analysis system that uses computational algorithms to objectively evaluate blood flow and tissue perfusion. This substitution eliminates human subjectivity while maintaining operational efficiency through automated processing of imaging data.
Solution Approach 2:
The patent introduces an intermediary computational analysis layer between the imaging data and the clinical assessment. This intermediary system processes raw imaging data through standardized algorithms, generating objective quantitative metrics that serve as a bridge between the complex imaging data and the clinical decision-making process.
2Loss of time
If qualitative visual evaluation is used, then the assessment is quick to perform, but it does not support standardized protocols for comparing perfusion status over time
Solution Approach 1:
The patent transforms the assessment from qualitative visual parameters to quantitative measured parameters. By converting perfusion status into numerical metrics through automated analysis, the system enables standardized protocols while maintaining efficiency. The quantitative parameters can be consistently compared across different time points and patients.
3Measurement precision
If machine learning algorithms are used to analyze fluorescence images, then the characterization of tissue perfusion becomes more accurate and objective, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex machine learning analysis into distinct modular components: image preprocessing module, feature extraction module, clustering analysis module, and interpretation module. This segmentation reduces system complexity by making each component independently manageable while maintaining the overall accuracy of tissue characterization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables consistent and objective characterization of tissue perfusion, reducing ambiguity and subjectivity, and allows for reliable tracking of perfusion status over multiple imaging sessions, facilitating more accurate clinical decision-making.
Implementation Method 1
Fluorescence imaging technologies typically employ the administration of a bolus of an imaging agent (such as for example, indocyanine green (ICG)) that subsequently circulates throughout the subject's tissue, e.g., vasculature and/or lymphatic system, and emits a fluorescence signal when illuminated with the appropriate excitation light.
Data Source
AI summary
Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.