Pathology Image Analysis for Targeted Diagnostic Test Selection
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Solution Overview
Problem
Diagnostic testing for diseases often goes unnoticed due to factors like unfamiliarity with testing, unavailability, 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, identifying applicable diagnostic tests based on prerequisite conditions, and outputting them to a digital storage or display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic testing is performed to identify therapies, then treatment efficacy is improved, but cost increases
Solution Approach 1:
The system performs preliminary analysis of digital pathology images to identify which diagnostic tests are likely to be beneficial before the tests are actually ordered. By pre-screening patients using machine learning analysis of their pathology specimens, the system predicts which tests have high probability of yielding actionable results, thereby avoiding unnecessary testing and reducing costs while maintaining treatment efficacy.
2Measurement precision
If comprehensive diagnostic testing is conducted, then treatment accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary screening of patients using machine learning analysis of digital pathology images to identify which diagnostic tests are most likely to provide valuable information. This pre-assessment allows clinicians to focus on a smaller, more targeted set of tests rather than conducting comprehensive testing on all patients, thereby reducing time consumption while maintaining treatment accuracy for those who need it.
3Loss of information
If more diagnostic tests are performed, then information completeness is improved, but complexity of testing increases
Solution Approach 1:
The system performs preliminary analysis to identify the specific information needs of each patient based on their digital pathology images. By predicting which tests are most likely to yield actionable results for individual patients, the system ensures that only necessary tests are ordered, maintaining information completeness while reducing the overall complexity of the testing process.
4Adaptability or versatility
If diagnostic tests are identified and prioritized using machine learning, then test applicability is improved, but system complexity increases
Solution Approach 1:
The system replaces manual clinical decision-making with machine learning algorithms that analyze digital pathology images to identify applicable diagnostic tests. The machine learning model automatically processes patient data and pathology images to predict test applicability, replacing the need for manual review by multiple clinicians while improving consistency and adaptability across different patient cases.
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.