AI Diagnostic Feature Detection in Digital Pathology
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Solution Overview
Problem
Pathologists face significant challenges in efficiently reviewing whole slide images (WSI) of pathology specimens, as they must manually analyze vast amounts of data, leading to time-consuming and error-prone processes, particularly with the decreasing number of pathologists and increasing specimen volume.
Innovation Solution
The implementation of a system and method that utilizes machine learning models to identify and highlight relevant diagnostic features on digitized pathology images, allowing pathologists to focus on specific areas of interest rather than reviewing the entire image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pathologists manually review entire whole slide images, then diagnostic completeness is maintained, but time consumption and workload increase significantly
Solution Approach 1:
The system segments the large whole slide image into multiple smaller patches and processes them in parallel using distributed computing resources. This allows comprehensive review of all image regions while reducing the time burden on pathologists by automating the analysis of individual patches and aggregating results.
Solution Approach 2:
An AI-based computational intermediary automatically analyzes the whole slide image, identifying candidate regions of interest and generating a preliminary report. This intermediary system handles the time-consuming manual review task, allowing pathologists to focus on verifying and finalizing diagnoses based on AI-generated insights.
2Reliability
If pathologists review entire whole slide images, then all diagnostic features are captured, but productivity decreases due to vast data volume
Solution Approach 1:
The system extracts only the most relevant diagnostic features and regions from the entire whole slide image using AI algorithms. By identifying and isolating key pathological features such as abnormal cells, tissue architecture patterns, and diagnostic markers, the system captures essential diagnostic information while eliminating the need to manually review all pixels, thereby increasing productivity.
Solution Approach 2:
The AI system performs preliminary analysis of the whole slide image before pathologist review, pre-identifying regions of interest and potential diagnostic features. This preliminary action filters out non-diagnostic areas, allowing pathologists to focus their expertise on a reduced set of critical regions, thus maintaining feature detection completeness while improving diagnosis throughput.
3Loss of time
If machine learning models are used to identify relevant features, then review time is reduced, but system complexity increases
Solution Approach 1:
The system employs a universal AI platform that can perform multiple diagnostic tasks across different pathology types using the same underlying infrastructure. The machine learning models are designed to handle various tissue types, diseases, and diagnostic questions through a unified architecture, reducing system complexity compared to having separate specialized systems for each diagnostic scenario.
Solution Approach 2:
The system uses pre-trained machine learning models that have been developed and validated on large datasets of processed images and patient data. These pre-trained models serve as reusable computational artifacts that can be applied to new cases without requiring complex custom model development for each scenario, thereby reducing system complexity while maintaining fast analysis performance.
Data Source
AI summary
Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.