Whole Slide Image Preanalytic Adjustment for Artifact Simulation

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Solution Overview

Problem

Existing image processing systems in pathology face challenges in preanalytic quality control due to variations in slide preparation and scanning, leading to artifacts like air bubbles, scan lines, and blur, which current methods struggle to simulate effectively, resulting in inconsistent performance across different laboratories.

Innovation Solution

Utilizing artificial intelligence (AI) to synthetically adjust scanned slide images by introducing or removing artifacts, simulating preanalytic variations such as staining methods, slice thickness, and other quality issues, thereby creating synthetic whole slide images that can be used to train AI systems to recognize and adapt to diverse preparation protocols.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Stability of the object's composition

If preanalytic quality control procedures are standardized in a given lab, then consistency of slide preparation is improved, but adaptability to different laboratory norms and geographic locales deteriorates

Engineering Contradiction:

Improveconsistency of slide preparationVSAvoidadaptability to different laboratory norms

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary analysis of the input whole slide image to identify preanalytic quality issues before diagnostic processing. By detecting artifacts, stains, and preparation quality in advance, the system can apply targeted corrections or adjustments prior to diagnostic AI analysis, thus maintaining consistency while adapting to different laboratory norms through flexible preprocessing

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If AI systems are trained on diverse slide preparations from multiple laboratories, then adaptability to different conditions is improved, but measurement precision of diagnostic features deteriorates due to increased variability

Engineering Contradiction:

Improveadaptability to different conditionsVSAvoidprecision of diagnostic features

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system segments the slide preparation process into distinct preanalytic stages (tissue sectioning, staining, mounting, scanning) and identifies quality issues at each stage separately. This segmentation allows the AI to understand and adapt to different laboratory protocols while maintaining precision by focusing on diagnostic features within each standardized segment

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adjusts processing parameters dynamically based on detected preanalytic conditions. By changing parameters such as contrast enhancement, artifact suppression levels, and diagnostic feature extraction settings according to the specific slide quality and laboratory protocol detected, the system maintains both adaptability and measurement precision

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If preanalytic artifacts such as air bubbles, scan lines, and blur are removed through repreparation or rescanning, then diagnostic quality is improved, but productivity and time efficiency deteriorate

Engineering Contradiction:

Improvediagnostic quality of slideVSAvoidslide processing efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system creates a digital copy of the physical slide through high-resolution scanning and uses AI-based image processing to remove or correct preanalytic artifacts in the digital domain. This approach maintains diagnostic quality by eliminating artifacts computationally rather than requiring physical repreparation or rescanning, thus preserving productivity

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system replaces mechanical preanalytic correction methods (repreparation, rescanning) with computational image processing techniques. By substituting physical manipulation with digital artifact removal algorithms, the system achieves high diagnostic quality without the time loss and productivity reduction associated with repeating physical preparation steps

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260044936A1Systems and methods for processing electronic images with preanalytic adjustment

Publication Date: 2026.02.12 PAIGE AI INC

AI summary

A method for processing electronic medical images may include receiving an initial whole slide image of a pathology specimen, receiving information about slide quality aspects to modify, and generating a synthetic whole slide image by applying a machine learning model to modify the received initial whole slide image according to the received information. The pathology specimen may be associated with a patient. The synthetic whole slide image may have a reduced quality as compared to the initial whole slide image.