Telepathology Image Routing Using AI Pre-Filtering
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Solution Overview
Problem
Large telepathology networks face challenges in efficiently routing pathology studies to the optimal pathologist for rapid and accurate diagnosis due to their complexity, spanning multiple time zones and subspecialties, with existing manual systems becoming unworkable.
Innovation Solution
A computing device-executed analysis module uses AI and metadata to pre-filter images, identifying 'normal' studies for deprioritization and determining the availability and suitability of pathologists, optimizing the transmission of suspect images for review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all pathology images are transmitted to pathologists for review, then diagnostic accuracy is maintained, but data transmission volume and review time increase significantly
Solution Approach 1:
The analysis module performs preliminary automated analysis of pathology images before they reach pathologists, pre-filtering out normal cases and identifying suspect images. This preliminary action reduces the workload for pathologists while maintaining diagnostic accuracy, as they only need to review the subset of images flagged as potentially abnormal.
Solution Approach 2:
An AI-based analysis module acts as an intermediary between image acquisition and pathologist review. This intermediary automatically analyzes images, identifies suspect cases, and prioritizes them for human review, thereby reducing the overall review time while maintaining diagnostic reliability through a two-stage process.
2Reliability
If all pathology images are transmitted to pathologists, then comprehensive review is ensured, but network data transmission volume increases
Solution Approach 1:
The system extracts and separates suspect images from the complete pathology study, transmitting only the relevant subset to pathologists. This extraction process removes unnecessary data (normal images) from the transmission pipeline, reducing network data volume while ensuring comprehensive review of only the clinically relevant cases.
3Ease of operation
If manual routing systems are used to assign pathology studies to pathologists, then pathologist selection is simple, but system complexity increases and becomes unworkable at scale
Solution Approach 1:
The automated analysis module performs self-service by automatically analyzing images, identifying suspect cases, and routing them to appropriate pathologists based on predefined criteria. This self-service capability eliminates the need for complex manual routing systems while maintaining ease of operation, as the system autonomously handles the complexity of large-scale pathologist assignment.
4Productivity
If automated analysis pre-filters images to identify suspect cases, then review time and data transmission are reduced, but analysis module complexity increases
Solution Approach 1:
The patent replaces the mechanical system of manual pathologist review with an automated analysis module that uses computational algorithms to pre-filter images. This substitution increases productivity by automating the initial screening process, while the complexity is managed through software rather than mechanical means, allowing for scalable deployment.
Data Source
AI summary
Systems and methods for optimizing the performance of telepathology image analysis in a distributed computing environment are discussed.