Hematology Analyzer with Digital Microscopy for Morphological Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hematology analyzers struggle with accuracy when morphological abnormalities are present in blood samples, leading to potential misdiagnosis and inappropriate treatment.
Innovation Solution
A system that integrates a hematology analyzer with a digital microscopy system and a computing device, using image recognition machine-learning logic to identify cell attributes and adjust diagnostic parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated hematology analysis is performed using flow cytometry and impedance, then quantitative reporting precision is improved, but accuracy deteriorates when morphological abnormalities are present
Solution Approach 1:
The patent combines automated hematology analysis (flow cytometry/impedance) with digital microscopy imaging and machine learning processing into an integrated system. The imaging system captures morphological data while the ML algorithm correlates it with quantitative parameters, allowing the system to maintain precision while improving accuracy for abnormal samples through multi-parameter correlation and pattern recognition.
Solution Approach 2:
The machine learning algorithm serves as an intermediary between the automated analyzer and the final diagnostic report. It processes both quantitative data from flow cytometry/impedance and morphological data from imaging, mediating the correlation between these data types to produce accurate diagnostic reports even when morphological abnormalities are present.
2Reliability
If manual microscopy review is performed to identify morphological abnormalities, then diagnostic accuracy is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs self-service by using machine learning algorithms to automatically review and identify morphological abnormalities from digital microscopy images. The ML model processes images independently, automatically flagging abnormal cells and generating corrected quantitative reports without requiring manual pathologist review, thus maintaining high accuracy while significantly improving throughput.
Solution Approach 2:
The patent replaces the mechanical manual microscopy review process with an automated digital imaging and machine learning system. Instead of human eyes and manual analysis, the system uses digital cameras, image processing algorithms, and ML models to automatically detect and report morphological abnormalities, eliminating the time-consuming manual process while maintaining diagnostic accuracy.
3Adaptability or versatility
If multiple separate analysis systems are used to cover all diagnostic parameters, then completeness of diagnostic coverage is improved, but device complexity and operational difficulty worsen
Solution Approach 1:
The patent creates a universal hematology analysis system that performs multiple functions through integration. The single system combines flow cytometry, impedance analysis, and digital microscopy imaging, with machine learning algorithms that handle multiple diagnostic tasks including quantitative reporting, morphological assessment, and abnormality detection. This multi-functional approach provides comprehensive diagnostic coverage while reducing the need for multiple separate systems.
Data Source
AI summary
A method for detecting one or more conditions in a blood sample is disclosed. The method includes (i) receiving cell data from one or more sensors communicatively coupled to the first computing device; (ii) determining via a first machine learning model diagnostic data associated with a first portion of the blood sample, wherein the diagnostic data comprises one or more identifiable parameters associated with blood cells; (iii) receiving an image of a plurality of cells of a second portion of the blood sample; (iv) determining via a second machine learning model, one or more attributes of the plurality of cells; (v) based on the determined one or more attributes of the plurality of cells, updating the one or more of the identifiable parameters; and (vi) retraining the first machine learning model using the updated one or more of the identifiable parameters.