Intelligent Vital Microscopy for Real-Time Microcirculation Analysis
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Solution Overview
Problem
Current clinical technologies cannot directly image capillaries in the microcirculation, instead measuring surrogates, which limits the ability to quantify microcirculatory function and provide quantitative information, making it difficult to diagnose diseases and determine therapeutic resolutions effectively.
Innovation Solution
An intelligent vital microscopy (IVM) device with a learning processor that processes images of the microcirculation to extract quantitative functional parameters, such as tissue red blood cell perfusion and vessel density, and identifies disease states, pathogen presence, and optimal therapeutic strategies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical technologies measure surrogates of microcirculatory flow and oxygenation, then measurement can be performed, but quantitative information and capillary imaging capability are lost
Solution Approach 1:
The patent replaces indirect measurement methods (laser Doppler, spectrophotometry) with direct optical imaging using a microscope-based system. This substitution enables actual visualization and quantitative measurement of capillaries rather than relying on surrogate metrics, thereby resolving the contradiction between measurement capability and information loss.
Solution Approach 2:
The patent introduces an image processing and analysis system as an intermediary between the microscope imaging and clinical diagnosis. This intermediary processes raw images to extract quantitative parameters (capillary density, blood flow velocity, oxygen saturation), enabling both direct imaging and quantitative measurement simultaneously.
2Measurement precision
If hand-held vital microscope images are analyzed offline using specialized software, then detailed microcirculatory information can be obtained, but real-time diagnostic capability is lost
Solution Approach 1:
The patent performs preliminary processing of microcirculation images by capturing multiple frames and pre-processing them with noise reduction and enhancement algorithms before analysis. This preliminary action prepares the data in advance, enabling faster real-time processing and analysis when diagnostic decisions are needed.
Solution Approach 2:
The patent implements a feedback loop where the analysis system continuously processes incoming image streams, provides real-time diagnostic feedback, and adjusts processing parameters dynamically. This feedback mechanism enables both detailed parameter extraction and real-time diagnostic capability to coexist.
3Loss of information
If multiple functional parameters of microcirculation are measured, then comprehensive diagnostic information is obtained, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional analysis system that can simultaneously measure multiple parameters (capillary density, blood flow velocity, oxygen saturation, hematocrit) using a single integrated platform. The system uses multiple imaging modes and analysis algorithms that work together, reducing overall system complexity compared to separate dedicated devices for each parameter.
Solution Approach 2:
The patent combines multiple measurement functions (optical imaging, spectroscopy, flow analysis) into a single integrated microscope-based system. By merging these functions that share common hardware components (light source, detector, optics), the system achieves comprehensive diagnostic capability without proportionally increasing complexity.
Data Source
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AI summary
An intelligent vital microscopy, IVM, device is described. The IVM device includes: a receiver configured to receive at least one IVM image of a human microcirculation, MC, of an organ surface; a learning processor coupled to the receiver and configured to: process the at least one IVM image and extract at least one MC variable therefrom, and identify from the extracted at least one MC variable of the at least one IVM image at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a medical state of the human; a presence of a pathogen; and an output coupled to the learning processor and configured to output the identification.