AI Triage for Digital Pathology Workflow Efficiency
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Solution Overview
Problem
Current digital pathology workflows face challenges in efficiently categorizing and diagnosing tissue specimens due to the complexity and variability of tissue types, requiring advanced visualization and interaction tools to aid pathologists in accurate diagnosis.
Innovation Solution
A computer-implemented method using a machine learning model to categorize digital pathology specimens by identifying suspicious or non-suspicious tissue, generating reports, and prioritizing cases based on complexity, with features like outlining regions, measuring tissue areas, and providing alphanumeric outputs for biomarker detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of all tissue specimens is performed, then diagnostic accuracy is maintained, but diagnostic efficiency and productivity decrease
Solution Approach 1:
The patent introduces an AI-based image analysis system as an intermediary between the tissue specimen and the pathologist. The AI model pre-analyzes whole slide images, identifies suspicious regions, and prioritizes cases for review, serving as a mediator that filters and prepares information for the pathologist. This resolves the contradiction by maintaining diagnostic accuracy through pathologist review of AI-prioritized cases while significantly improving productivity through automated triage.
Solution Approach 2:
The system performs preliminary analysis of all tissue specimens using machine learning models before pathologist review. Cases are pre-categorized by complexity and suspicious regions are pre-identified, allowing pathologists to focus their expertise on cases that require human judgment. This preliminary action resolves the contradiction by automating routine assessment while preserving human oversight for complex decisions.
2Productivity
If AI automation is increased to improve productivity, then diagnostic efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the pathology workflow into distinct phases: automated preprocessing, AI-based triage, pathologist review, and report generation. Each phase is handled by specialized components with defined responsibilities. This segmentation resolves the complexity issue by creating modular, manageable subsystems rather than a monolithic complex system, making the AI automation more controllable and maintainable.
Solution Approach 2:
The AI system performs multiple functions including image quality assessment, case prioritization, region of interest detection, and report generation. This multi-functionality resolves the complexity contradiction by consolidating multiple automated tasks into a single integrated system rather than requiring separate complex systems for each function, improving workflow efficiency while managing complexity through unified architecture.
3Measurement precision
If comprehensive analysis of all case features is performed, then measurement precision improves, but processing time increases
Solution Approach 1:
The system performs partial analysis by focusing computational resources on identifying and analyzing only the most suspicious regions and highest-risk cases rather than exhaustively analyzing every pixel and feature. The AI model uses confidence thresholds to determine when partial analysis is sufficient, resolving the contradiction by achieving adequate detection accuracy for critical cases while reducing processing time through selective rather than comprehensive analysis.
Data Source
AI summary
A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.