Augmented Reality Microscope Overlaying Biomarker Data
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Solution Overview
Problem
The 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 digital slides stained with hematoxylin and eosin, which can lead to inaccurate assessments of cancer presence and staging.
Innovation Solution
An augmented reality microscope system that captures digital images of biological samples through an eyepiece, uses machine learning pattern recognizers to identify areas of interest, and overlays quantitative data in real-time, assisting pathologists in classifying samples by highlighting cancerous cells or other specific features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of digital slides is used, then pathologists can classify biological samples, but the process is time-consuming and error-prone due to reader fatigue
Solution Approach 1:
The patent introduces an augmented reality microscope system with machine learning pattern recognizers as an intermediary between the digital slide and the pathologist. The system captures images through the eyepiece, processes them with AI algorithms to identify areas of interest and quantify biomarkers, then overlays this information onto the eyepiece view. This intermediary system handles the time-consuming manual scanning and initial analysis, while the pathologist focuses on interpreting the AI-assisted findings, thereby reducing both examination time and error rates.
Solution Approach 2:
The patent replaces the mechanical manual examination process with an automated optical and computational system. Instead of the pathologist manually scanning slides and identifying features, the system uses a camera to capture images through the eyepiece, machine learning algorithms to automatically detect and classify areas of interest, and an augmented reality display to present results. This substitution of mechanical human effort with automated systems directly addresses the time loss and fatigue issues while maintaining or improving classification accuracy.
2Measurement precision
If deep learning techniques are applied to digital tissue images, then cancer diagnosis accuracy improves, but the system complexity increases
Solution Approach 1:
The patent designs the augmented reality microscope system to perform multiple functions through a single integrated platform. The same eyepiece-based camera system and machine learning pipeline that provides real-time classification assistance also generates quantitative biomarker data, identifies areas of interest, and presents results through augmented reality. This multi-functional design avoids the need for separate complex systems for each function, thereby improving diagnosis accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The augmented reality display acts as an intermediary that simplifies the presentation of complex machine learning outputs to the pathologist. Instead of requiring the pathologist to interact with complex software interfaces or analyze raw algorithm outputs, the system translates complex deep learning results into intuitive visual overlays showing areas of interest, confidence levels, and quantitative measurements directly on the microscope view. This intermediary layer masks the underlying system complexity while delivering high diagnostic accuracy.
3Measurement precision
If quantitative biomarker data is overlaid in real-time, then diagnostic accuracy improves, but the processing speed requirements increase
Solution Approach 1:
The system performs preliminary processing of image data through the eyepiece before it reaches the pathologist. The camera continuously captures images, and the machine learning pattern recognizers pre-identify areas of interest, pre-quantify biomarkers, and pre-generate overlay information. This preliminary action allows the system to have processing results ready in real-time when the pathologist views the sample, thereby achieving both high diagnostic accuracy through quantitative data and real-time processing speed without requiring excessive computational power during the actual examination.
Data Source
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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, or quantitative biomarker data, 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.