AI Digital Pathology Slide Classification and Metadata Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computational pathology methods face challenges in efficiently and accurately diagnosing cancer and other diseases, particularly in terms of time-consuming manual processes and the need for additional testing to confirm diagnoses.
Innovation Solution
The development of an integrated computing platform that utilizes artificial intelligence (AI) to classify digital pathology slides, automate the generation of metadata, and facilitate the viewing and transfer of images across geographic regions, while ensuring data security and compliance with regulatory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual pathology diagnosis processes are used, then diagnostic accuracy can be maintained through expert review, but the process becomes time-consuming and less efficient
Solution Approach 1:
The diagnostic process is segmented into multiple independent AI models that analyze different aspects of pathology images simultaneously. Each model focuses on specific features (e.g., cancer detection, grading, staging), allowing parallel processing and faster overall diagnosis while maintaining comprehensive evaluation
Solution Approach 2:
AI models perform preliminary analysis and generate diagnostic recommendations before final expert review. This preliminary action filters and prioritizes cases, allowing pathologists to focus on complex or uncertain cases while routine cases are processed more quickly through the automated analysis
2Reliability
If additional testing is performed to confirm diagnoses, then diagnostic reliability improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The system implements feedback loops where AI diagnostic results are automatically reviewed and validated by pathologists, and outcomes are fed back into the system for continuous improvement. This feedback mechanism enhances reliability by ensuring accurate diagnoses while reducing unnecessary additional testing through confident AI predictions
Solution Approach 2:
The AI system acts as an intermediary between raw pathology images and final diagnostic decisions. It provides structured analysis and confidence scores that help pathologists determine when additional testing is truly necessary, reducing unnecessary complexity while maintaining reliability
3Adaptability or versatility
If digital pathology images are transferred across geographic regions, then accessibility and collaboration improve, but data security and privacy protection become more challenging
Solution Approach 1:
The system implements nested security layers where encryption is applied at multiple levels: data encryption during transfer, secure storage encryption, and access control encryption. This nested approach allows images to be shared across regions while maintaining robust security protection at each layer
Solution Approach 2:
A secure cloud-based platform acts as an intermediary for transferring pathology images across geographic regions. The platform implements automated security protocols including authentication, authorization, and encrypted transmission, enabling regional collaboration while protecting patient data privacy
Data Source
AI summary
Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, including receiving, from a bridge, a whole slide image (WSI) and associated information, wherein the WSI is associated with a geographic region and depicts a specimen associated with a patient; storing the received WSI in a first encrypted bucket; determining, by artificial intelligence, whether portions of the specimen are suspicious for disease; generating metadata associated with the WSI based on whether portions of the specimen are suspicious for disease; and storing the metadata in a second encrypted bucket. The bridge may receive the WSI from a WSI system and may receive the associated information from a laboratory information system (LIS), and the WSI system and LIS may or may not be integrated.