AI-Enabled Pathological Slide Scanner for Real-Time Tissue Analysis
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Solution Overview
Problem
Current digital pathology slide scanners are limited to partial or whole slide image acquisition and digitization, lacking a singular unit capable of scanning and analyzing tissue samples efficiently, which hinders comprehensive tissue evaluation.
Innovation Solution
An AI-enabled pathological slide scanning unit that integrates custom algorithms and AI modules for real-time region of interest detection, cell quantification, and morphological measurements, enabling simultaneous scanning and digital analysis of tissue samples, with features like heatmap generation for quick identification of regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a singular unit integrates both scanning and analysis functions, then diagnostic efficiency is improved, but device complexity increases
Solution Approach 1:
The patent combines the slide scanning function and AI analysis function into a single integrated unit. The scanning module captures whole slide images while the AI module simultaneously performs cellular object classification, tumor identification, and quantification analysis on the acquired images, eliminating the need for separate analysis equipment and workflows.
Solution Approach 2:
The integrated unit performs multiple functions including whole slide scanning, real-time AI analysis, heatmap generation, and diagnostic reporting within a single device. The system can identify various cellular objects, quantify tumor cells, and generate visual overlays, making it a multi-functional diagnostic platform that replaces multiple separate tools.
2Loss of time
If real-time analysis is performed during scanning, then processing time is reduced, but computational requirements increase
Solution Approach 1:
The AI module is pre-trained with classification models for identifying cellular objects, tumor cells, and other pathological features before actual scanning. This preliminary training enables the system to perform rapid real-time analysis during scanning without requiring intensive computational resources at the moment of image acquisition, as the heavy computational work has already been done during model training.
Solution Approach 2:
The system performs continuous analysis throughout the scanning process rather than analyzing images after scanning is complete. The AI module processes images in real-time as they are acquired, maintaining continuous useful action that reduces overall processing time while distributing computational load across the scanning duration rather than concentrating it in a post-processing phase.
Data Source
AI summary
In a method of operation of an artificial intelligence-equipped specimen scanning and analysis unit to digitally scan and analyze pathological specimen slides, a sample slide with a mounted tissue sample is scanned and analyzed according to one or more user-selected algorithms in order to generate a heatmap visually depicting the presence of one or more user-selected sample attributes of the tissue sample. One or more artificial intelligence modules, including a deep learning computation module, is provided and can be trained by the user for future analysis of new samples. One or more regions of interest may be selected from the heatmap to include in the results of the analysis. A focus window may be used to closely inspect any given region of the whole slide image, and a trail map is generated from the movement of the focus window.