Pathology Image Analysis for Diagnostic Test Prioritization
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Solution Overview
Problem
Diagnostic testing for patients is often not performed due to factors such as unfamiliarity of doctors with testing, unavailability of 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 patient-specific information, availability, and cost considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic testing is performed to identify effective therapies, then treatment effectiveness is improved, but cost and complexity increase
Solution Approach 1:
The system performs preliminary analysis of digital pathology images to identify biomarkers and predict test outcomes before actual diagnostic testing is performed. This preliminary action helps determine which tests are likely to be beneficial, reducing unnecessary testing complexity while maintaining treatment effectiveness.
Solution Approach 2:
The machine learning system acts as an intermediary between pathology images and diagnostic testing decisions. It processes images to generate predictions about test outcomes, serving as a mediator that reduces the complexity of directly interpreting images and deciding on appropriate tests.
2Measurement precision
If comprehensive diagnostic testing is performed, then accuracy of therapy identification is improved, but time and resource consumption increase
Solution Approach 1:
The system performs partial analysis by focusing on specific biomarkers and features in pathology images that are most relevant to predicting test outcomes. Rather than comprehensive analysis of all possible features, it selectively examines key indicators, reducing time consumption while maintaining adequate accuracy for decision-making.
Solution Approach 2:
The machine learning model changes parameters by adjusting the threshold for test recommendation and selecting which biomarkers to prioritize based on the specific case. This dynamic parameter adjustment optimizes the balance between accuracy and time consumption for each individual patient evaluation.
3Adaptability or versatility
If multiple diagnostic tests are recommended, then completeness of treatment options is improved, but cost increases
Solution Approach 1:
The system applies local quality by recommending different sets of tests based on the specific characteristics of each patient's pathology image and clinical context. Rather than uniformly recommending all possible tests, it tailors the test panel to the specific needs of each case, improving treatment option completeness while reducing unnecessary costs.
Solution Approach 2:
The system performs preliminary evaluation to identify which tests are most likely to yield useful information for the specific patient. By pre-screening potential tests based on image analysis, it recommends only those tests with high probability of being beneficial, reducing overall testing cost while maintaining completeness of relevant treatment options.
Data Source
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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.