Machine Learning Specimen Interpretation System

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Solution Overview

Problem

Manual review of biologic specimens is time-consuming and prone to inconsistencies, leading to reduced accuracy and increased costs, as cytotechnicians and cytopathologists may not thoroughly examine all cells due to time constraints, and different reviewers may provide varying interpretations.

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 diagnosing diseases, providing automated analysis and reducing the need for extensive human review.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual review of biologic specimens is performed by cytotechnicians and cytopathologists, then diagnostic interpretation can be provided, but the review process becomes time-consuming and accuracy decreases due to time constraints limiting the number of cells reviewed

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidreview time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical review process with an automated digital image processing system that uses machine learning models to analyze biologic specimens. The system automatically detects cells, extracts features, and generates diagnostic interpretations, eliminating the time constraints that limit manual review while maintaining or improving diagnostic accuracy through comprehensive analysis of all cells in the specimen.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the digital image processing system to autonomously perform the entire diagnostic review process without human intervention. The machine learning models automatically analyze specimens, identify pathological features, and generate diagnostic reports, freeing human professionals from time-consuming manual review while providing consistent, comprehensive analysis of all specimen cells.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual reviewers examine specimens under time constraints, then review speed is maintained, but the number of cells reviewed is limited thereby decreasing accuracy

Engineering Contradiction:

Improvereview speedVSAvoiddiagnostic accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The automated digital image processing system with machine learning models replaces manual review, enabling simultaneous achievement of high productivity and high accuracy. The system can analyze all cells in a specimen without time constraints, performing comprehensive analysis that improves diagnostic accuracy while maintaining rapid processing speed through automated computation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs excessive action by analyzing all cells in the specimen rather than limiting review to a subset. This comprehensive analysis of every cell, rather than sampling, ensures no diagnostic features are missed while the automated system processes the entire specimen efficiently, achieving both complete coverage and rapid turnaround.

Inventive Principle:

Principle #16Partial or excessive action

3Adaptability or versatility

If different manual reviewers interpret the same specimen, then diverse perspectives may be considered, but interpretation consistency decreases across reviewers

Engineering Contradiction:

Improveinterpretation flexibilityVSAvoidinterpretation consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The patent replaces variable human interpretation with a standardized machine learning system that applies consistent diagnostic criteria to all specimens. The automated system eliminates inter-reviewer variability by using fixed algorithms and objective feature extraction, ensuring identical specimens receive identical interpretations while maintaining diagnostic flexibility through programmable analysis parameters.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Measurement precision

If extensive manual review is performed to improve accuracy, then diagnostic precision increases, but time consumption and costs increase

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The automated digital image processing system with machine learning models replaces manual review, enabling comprehensive analysis of all specimen cells without the time and cost penalties of extended human review. The system performs extensive analysis equivalent to reviewing every cell while maintaining rapid processing speed and reducing costs through automated computation rather than human labor.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11508168B2Systems and methods for specimen interpretation

Publication Date: 2022.11.22 UPMC

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.