Digital Pathology Image Analysis for Diagnostic Test Selection
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Solution Overview
Problem
Diagnostic testing for identifying therapies and courses of treatment for diseased tissues is often not performed due to factors such as unfamiliarity of doctors with testing, unavailability of testing facilities, lack of viable samples, low pre-test expectations, or high costs, leading to ineffective treatments.
Innovation Solution
A system and method using machine learning to process digital images of pathology specimens to identify and prioritize diagnostic tests based on prerequisite conditions, including availability and cost, by applying a trained machine learning system to determine applicable tests and output them to a digital storage or display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic testing is performed to identify effective therapies, then treatment efficacy is improved, but cost and complexity of the healthcare process increase
Solution Approach 1:
The system performs preliminary analysis of digital pathology images to identify prerequisite conditions for diagnostic tests before actual testing is conducted. By pre-assessing whether tests are likely to be applicable based on image features, the system avoids unnecessary testing and streamlines the healthcare process while ensuring appropriate tests are performed for improved treatment efficacy.
Solution Approach 2:
The machine learning system automatically evaluates digital images and determines test applicability without requiring manual review by pathologists for each potential test. This self-service capability reduces the burden on healthcare providers while maintaining high standards for determining which diagnostic tests should be performed.
2Loss of information
If comprehensive diagnostic testing is performed, then identification of effective therapies is improved, but time and resources are consumed
Solution Approach 1:
The system conducts preliminary evaluation of digital pathology images to predict which diagnostic tests have a high likelihood of being applicable and beneficial. This pre-screening process identifies the most promising tests before actual diagnostic testing begins, ensuring comprehensive therapy identification while minimizing unnecessary testing time and resource consumption.
Solution Approach 2:
Rather than performing all possible diagnostic tests uniformly, the system applies partial action by selectively identifying and prioritizing only those tests that are most likely to be applicable based on image analysis. This approach captures the essential information needed for therapy identification while avoiding the time and resource expenditure of exhaustive testing.
3Adaptability or versatility
If multiple diagnostic tests are considered, then comprehensive care is improved, but difficulty in selecting appropriate tests increases
Solution Approach 1:
The system provides feedback to healthcare providers by presenting a prioritized list of diagnostic tests with predictions of applicability based on digital image analysis. This feedback mechanism translates complex image features and test criteria into actionable recommendations, making it easier for providers to select appropriate tests while maintaining comprehensive care considerations.
Solution Approach 2:
The machine learning system acts as an intermediary between the large set of available diagnostic tests and the healthcare provider's decision-making process. It mediates the complexity by automatically evaluating which tests are most likely to be applicable based on image features, thereby simplifying the selection process while ensuring comprehensive care is maintained through systematic evaluation of multiple test options.
Data Source
AI summary
Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.