Automated Biologic Specimen Interpretation via Machine Learning
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Solution Overview
Problem
Manual review of biologic specimens under microscopy is time-consuming and prone to variability, leading to potential inaccuracies and inconsistencies in disease diagnosis due to time constraints and differing interpretations among reviewers.
Innovation Solution
A system and method that utilize digital image processing to identify feature vectors for cells, generate feature scores, and classify cells using machine learning models to assist in the interpretation of biologic specimens, providing automated diagnostics and reducing the need for extensive human review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of biologic specimens is performed by cytotechnicians and cytopathologists, then diagnostic accuracy can be maintained through human expertise, but the process is time-consuming and produces variable results due to reviewer differences and time constraints
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system that uses digital image processing to analyze cell morphology and classify specimens. The system extracts features from digital images of cells and tissues, processes them through trained models, and generates diagnostic outputs without requiring manual microscopic review, thereby eliminating the time loss associated with human review while maintaining diagnostic accuracy through consistent algorithmic application.
2Loss of time
If manual reviewers limit the number of cells or tissue portions reviewed due to time constraints, then review time is reduced, but accuracy decreases
Solution Approach 1:
The machine learning system performs self-service analysis by automatically processing entire specimen images without requiring selective sampling or limiting review scope. The system independently analyzes all detectable cells and tissue portions within the provided digital image, extracting and evaluating relevant features according to its trained criteria, thereby achieving comprehensive accuracy without the time constraints that force manual reviewers to limit their examination scope.
3Adaptability or versatility
If different manual reviewers characterize specimens, then multiple perspectives can be obtained, but consistency and agreement on diagnosis varies across reviewers
Solution Approach 1:
The patent implements homogeneity by replacing variable human judgment with a consistent machine learning algorithm that applies identical evaluation criteria to all specimens. The system uses standardized feature extraction and classification models that produce reproducible results regardless of which 'reviewer' processes the specimen, eliminating the variability inherent in different human reviewers' interpretations while maintaining the ability to handle diverse specimen types through the same unified framework.
Data Source
AI summary
Systems, methods, devices, and other techniques using machine learning for interpreting, or assisting in the interpretation of, biologic specimens based on digital images are provided. Methods for improving image-based cellular identification, diagnostic methods, methods for evaluating effectiveness of a disease intervention, and visual outputs useful in assisting professionals in the interpretation of biologic specimens are also provided.