Digital Pathology Image Navigation for Salient Region Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Pathologists face inefficiencies and fatigue in reviewing digital pathology slides due to the need for manual panning and zooming across large areas of tissue, which are often devoid of relevant information, leading to prolonged diagnostic review times and potential eye strain.
Innovation Solution
Implementing artificial intelligence to automatically determine and present salient attributes of digital pathology images, using tissue-specific policies to smoothly navigate the field of view, reducing the need for manual input and optimizing the diagnostic workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pathologists manually search slides for salient information, then diagnostic accuracy is maintained, but review time increases and pathologist fatigue occurs
Solution Approach 1:
An AI-based image processing system acts as an intermediary between the pathology slide and the pathologist. The system automatically analyzes whole slide images, detects salient regions and attributes, and presents filtered results to the pathologist, thereby reducing manual search time while maintaining diagnostic accuracy through AI-assisted identification of clinically relevant areas
Solution Approach 2:
The system performs preliminary analysis of pathology slides before pathologist review by automatically identifying salient regions, detecting tissue attributes, and prioritizing areas of clinical interest. This preliminary processing filters out irrelevant information, allowing pathologists to focus their expertise on already-identified relevant areas, thus reducing overall review time
2Reliability
If pathologists manually pan and zoom across large tissue areas, then comprehensive review is achieved, but eye strain and fatigue increase
Solution Approach 1:
The system extracts and isolates only the salient regions and clinically relevant attributes from large pathology slides, removing irrelevant background tissue and non-diagnostic areas. By presenting only the extracted relevant information to the pathologist, the system maintains comprehensive review capability while eliminating the need for prolonged manual panning and zooming that causes eye strain
Solution Approach 2:
The manual mechanical interaction of panning and zooming is replaced by an automated computational system that programmatically navigates through slide images, identifies regions of interest, and dynamically presents them to the pathologist. This substitution eliminates the repetitive manual eye movements and hand operations that lead to fatigue and eye strain
3Productivity
If AI automatically identifies salient attributes, then review efficiency increases, but system complexity increases
Solution Approach 1:
The AI system is segmented into specialized functional modules including image preprocessing components, salient region detection algorithms, attribute detection systems, and result presentation interfaces. Each module handles a specific aspect of the analysis independently, which manages overall system complexity while enabling high review efficiency through modular, maintainable architecture
Solution Approach 2:
The AI-based image processing system is designed as a universal platform capable of analyzing various types of pathology slides, detecting multiple tissue attributes, and adapting to different diagnostic requirements. This multi-functional design consolidates what would otherwise require multiple separate systems, managing complexity while providing comprehensive automated review capabilities across diverse pathology applications
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A computer-implemented method may identify attributes of electronic images and display the attributes. The method may include receiving one or more electronic medical images associated with a pathology specimen, determining a plurality of salient regions within the one or more electronic medical images, determining a predetermined order of the plurality of salient regions, and automatically panning, using a display, across the one or more salient regions according to the predetermined order.