AR Microscope Overlaying ML Heatmaps for Pathology
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Solution Overview
Problem
Current methods for classifying biological samples, such as lymph node biopsies for cancer, are time-consuming, error-prone, and suffer from reader fatigue and reliability issues due to manual examination of hematoxylin and eosin-stained slides, with limitations in sensitivity and accuracy.
Innovation Solution
A microscope system that captures digital images of biological samples and uses machine learning pattern recognizers to identify areas of interest, overlaying enhancements such as heat maps or annotations in real-time through the eyepiece, assisting pathologists in classifying cancerous cells or pathogens without disrupting the optical path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual examination of hematoxylin and eosin-stained slides is used, then pathologists can classify biological samples, but the process is time-consuming and error-prone with reader fatigue reducing sensitivity
Solution Approach 1:
The system performs preliminary scanning and identification of potential cancer cells using automated image processing and machine learning algorithms before the pathologist conducts detailed examination. This preliminary action filters and prioritizes areas of interest, reducing the time pathologists need to spend on routine scanning while maintaining high diagnostic accuracy.
Solution Approach 2:
An automated image analysis system acts as an intermediary between the biological sample and the pathologist. The system processes digital images, identifies suspicious areas, and presents them to the pathologist for final interpretation, thereby reducing examination time and human error while maintaining or improving diagnostic reliability.
2Reliability
If manual examination methods are used, then pathologists can assess lymph nodes for metastasis, but intra and inter-grader reliability negatively impact sensitivity
Solution Approach 1:
The system incorporates feedback mechanisms where examination results from multiple pathologists are compared and analyzed. The system provides feedback on areas of disagreement, allows for re-examination of ambiguous cases, and continuously learns from accumulated data to improve consistency and reliability of assessments across different pathologists.
Solution Approach 2:
The patent replaces manual mechanical examination with automated digital image processing and analysis systems. This substitution eliminates human variability in initial assessment, provides consistent measurement criteria, and enhances detection sensitivity through algorithms designed to identify subtle patterns that may escape human observation.
3Measurement precision
If expensive whole slide scanners are used, then digital images can be obtained for analysis, but the cost is high
Solution Approach 1:
Instead of scanning entire whole slides at high resolution, the system performs partial scanning only of regions identified as potentially containing cancer cells. This selective approach maintains high measurement precision for critical areas while significantly reducing the overall cost and time required compared to complete whole slide scanning.
Solution Approach 2:
The examination process is segmented into multiple stages: initial low-resolution screening, identification of suspicious regions, and focused high-resolution analysis of those specific areas. This segmentation allows the system to achieve high measurement precision where needed while minimizing overall resource expenditure and cost.
Data Source
AI summary
A microscope of the type used by a pathologist to view slides containing biological samples such as tissue or blood is provided with the projection of enhancements to the field of view, such as a heatmap, border, or annotations, substantially in real time as the slide is moved to new locations or changes in magnification or focus occur. The enhancements assist the pathologist in characterizing or classifying the sample, such as being positive for the presence of cancer cells or pathogens.