AI Pathology Reporting for Tissue Measurement and Quality Scoring
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Solution Overview
Problem
Current surgical pathology processes are manual, labor-intensive, and lack standardized quality control mechanisms, leading to inefficiencies, errors, and variations in tissue analysis, particularly in measuring tissue size, inking, and slide quality, which can impact diagnostic accuracy and patient care.
Innovation Solution
A system and method utilizing artificial intelligence and deep learning to automate tissue size determination, inking, and slide quality assessment, employing convolutional and graph neural networks to analyze whole slide images, detect tissue artifacts, and generate automated pathology reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement and analysis of tissue size is performed by histotechnicians, then measurement can be performed, but labor intensity increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical measurement by histotechnicians with an automated image processing system that uses computer vision algorithms to measure tissue size from digital images, eliminating manual labor while maintaining measurement accuracy
Solution Approach 2:
The system automatically performs tissue size measurement and quality assessment without requiring manual intervention, allowing the process to serve itself through automated detection and analysis algorithms
2Manufacturing precision
If manual inking and tissue segmentation is performed, then tissue can be processed, but time consumption increases and productivity decreases
Solution Approach 1:
The patent replaces manual inking and segmentation procedures with automated image processing algorithms that digitally identify and segment tissue regions based on color, texture, and morphological features, eliminating manual labor and reducing processing time
Solution Approach 2:
The system performs preliminary automated detection and segmentation of tissue structures before final analysis, preparing the data in advance to streamline subsequent processing steps and reduce overall time consumption
3Reliability
If subjective tissue quality assessment is performed by pathologists, then quality can be evaluated, but variability increases and reliability decreases
Solution Approach 1:
The patent replaces subjective human-based quality assessment with objective automated image analysis algorithms that consistently evaluate tissue quality parameters such as staining quality, section integrity, and artifact detection, eliminating variability while maintaining comprehensive assessment capability
Solution Approach 2:
The system incorporates automated feedback mechanisms that continuously monitor and evaluate tissue quality parameters, providing real-time assessment results that can trigger quality control actions and ensure consistent standards are maintained across all samples
4Productivity
If automated AI-based analysis is implemented, then productivity increases and manual labor decreases, but system complexity increases
Solution Approach 1:
The patent divides the complex AI-based pathology analysis system into separate modular components, each handling specific tasks such as image acquisition, preprocessing, tumor detection, grading, and report generation, making the overall complex system more manageable and maintainable
Solution Approach 2:
The system employs a multi-functional AI platform that can perform various pathology tasks including tumor detection, classification, grading, and quality assessment using a unified architectural framework, reducing the need for multiple separate specialized systems
Data Source
AI summary
This invention provides a system for automation of a pathology process, which includes a processor having trained artificial intelligence (AI) modules operating in association therewith, adapted to receive image data from camera images of whole tissue acquired by a camera assembly and whole slide images (WSIs) of inked and segmented tissue samples. A mask produces image results for tissue with holes and free of holes, and a filter provides filtered image results to the AI modules, detecting tumors and macroarchitecture features. A quality assessment process produces quality score outputs for tumors and macroarchitecture features. A report generator provides reports with one or more parameters to a user via an interface. More particularly, the report generator automatically creates a pathology report, having a written description and pictorial diagram relative to the gross images of the tissue integrating the outputs of the AI modules used to analyze the whole slide digital images.