Machine Learning Slide Deficiency Detection for Digital Pathology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing pathology slide preparation process is time-consuming and inefficient, often resulting in slides with insufficient information for diagnosis, leading to delays and unnecessary additional tests.
Innovation Solution
A computer-implemented method using machine learning to analyze digital pathology images, determining deficiencies in tissue specimens and automatically ordering additional slides for preparation, thereby ensuring sufficient information for diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional slides are ordered manually after pathologist review, then more information can be gathered for diagnosis, but the process is time-consuming and delays final diagnosis
Solution Approach 1:
The machine learning system performs preliminary analysis of the original slide image to predict whether additional slides are needed before the pathologist reviews the slide. This advance assessment allows the system to automatically order additional slides proactively, eliminating the delay between manual review and additional testing ordering, thereby reducing overall diagnostic time while ensuring sufficient information is gathered.
Solution Approach 2:
The system uses machine learning models that are trained on historical data to provide feedback predictions about diagnostic sufficiency. The ML system analyzes features from the original slide and automatically determines whether additional slides are needed, creating a feedback loop that continuously monitors diagnostic readiness and triggers additional testing only when necessary, optimizing both time and information sufficiency.
2Ease of operation
If manual slide preparation is used, then pathologists can review slides, but slides may lack sufficient information requiring additional tests
Solution Approach 1:
The machine learning system performs self-service by automatically analyzing the original slide images and determining whether additional slides are needed without requiring pathologist intervention. The system independently assesses diagnostic sufficiency based on trained models and automatically triggers additional slide preparation only when information gaps are detected, ensuring comprehensive diagnostic information while maintaining ease of operation.
Solution Approach 2:
The machine learning system acts as an intermediary between the slide preparation process and the pathologist review process. It analyzes the original slide, predicts diagnostic sufficiency, and automatically orders additional slides when needed, serving as a mediator that ensures sufficient information is gathered before pathologist review without requiring direct pathologist involvement in the assessment decision.
3Reliability
If additional slides are prepared without automated prediction, then pathologists can gather more information, but material waste increases and productivity decreases
Solution Approach 1:
The machine learning system performs preliminary analysis of the original slide to predict the need for additional slides before they are prepared. This advance assessment prevents unnecessary slide preparation by only triggering additional slides when the ML model predicts diagnostic insufficiency, thereby reducing material waste and improving productivity while maintaining reliable diagnostic information.
Solution Approach 2:
The system changes the decision parameter from manual pathologist judgment to automated machine learning prediction. The ML model uses trained parameters and features from original slides to automatically determine the need for additional slides, optimizing the balance between diagnostic sufficiency and preparation efficiency by applying data-driven criteria rather than reactive manual decisions.
Data Source
AI summary
Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.