Digital Pathology Image Analysis for Automated Slide QC
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Solution Overview
Problem
Existing pathology workflows are inefficient and time-consuming due to the need for manual slide preparation and diagnosis, with potential delays and inaccuracies in identifying specimen properties and categorizing tissue types, and there is a lack of integrated systems for automated quality control and quality assurance.
Innovation Solution
A computer-implemented method using machine learning systems to analyze digital pathology images, identifying specimen characteristics and areas of interest, integrated with a workflow that includes automated slide preparation, quality control, and quality assurance, utilizing machine learning algorithms and AI tools for efficient specimen analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual slide preparation and diagnosis methods are used, then pathologists can perform comprehensive analysis, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical slide preparation and visual inspection with automated digital imaging systems and machine learning algorithms. The whole slide imaging system captures digital images of pathology slides, and AI models automatically analyze tissue characteristics, replacing the mechanical and visual processes performed by pathologists with computational systems.
Solution Approach 2:
The system enables self-service through automated quality control and quality assurance processes. The machine learning models automatically assess slide quality, detect artifacts, and validate diagnostic findings without requiring continuous manual intervention, allowing the system to perform self-validation and quality monitoring.
2Measurement precision
If manual specimen analysis is performed, then detailed examination is possible, but errors and inaccuracies may occur
Solution Approach 1:
The system implements feedback loops where machine learning models continuously learn from annotated pathology images and diagnostic outcomes. The quality assurance module provides feedback on detection accuracy, and the system refines its algorithms based on validated results, improving precision and reliability over time through iterative learning and validation.
Solution Approach 2:
The system performs preliminary quality control assessments and artifact detection before final diagnosis. The machine learning models pre-screen slides for quality issues, staining problems, and artifacts that could affect diagnostic accuracy, allowing corrections to be made before comprehensive analysis occurs.
3Reliability
If comprehensive quality control and quality assurance processes are implemented, then diagnostic reliability improves, but system complexity increases
Solution Approach 1:
The patent merges quality control and quality assurance functions into the same automated digital pathology workflow. The machine learning system performs both QC (technical quality assessment) and QA (diagnostic accuracy validation) in an integrated manner, combining multiple functions into a unified system that reduces operational complexity while maintaining comprehensive quality oversight.
Solution Approach 2:
The machine learning system performs multiple functions including image acquisition, quality assessment, artifact detection, tissue classification, and diagnostic support within a single platform. This multi-functional approach consolidates what would otherwise require separate systems and processes, reducing overall workflow complexity while maintaining comprehensive quality control.
Data Source
AI summary
Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target electronic image, the machine learning system having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the target electronic image identifying an area of interest based on the at least one characteristic of the target specimen and/or the at least one characteristic of the target electronic image.