Medical Image Prioritization via Uncertainty Scoring
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Solution Overview
Problem
Current medical image prioritization methods, relying on manual or rule-based approaches, fail to effectively identify critical conditions due to ambiguous labeling and lack of uncertainty consideration, leading to delays in critical care and potential harm to patients.
Innovation Solution
A machine learning-based system that trains a model using statistical parameters like predictive value, uncertainty, and noise standard deviation to calculate a likelihood score for medical images, prioritizing images with high severity and uncertainty, ensuring timely evaluation of critical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual or rule-based approaches are used for prioritizing medical images, then the system complexity is low, but the effectiveness of identifying critical conditions deteriorates due to ambiguous labeling schemes and lack of uncertainty consideration
Solution Approach 1:
The patent replaces manual prioritization and simple rule-based systems with a machine learning model that automatically analyzes medical images. The model uses statistical parameters including uncertainty measurements to prioritize images, substituting human judgment and basic rules with an automated intelligent system that handles ambiguous cases more effectively.
Solution Approach 2:
The patent introduces uncertainty as a new parameter in the prioritization process. By calculating statistical parameters including uncertainty for each medical image output, the system transforms the prioritization from simple rule-based sorting to a multi-parameter evaluation that accounts for model confidence, thereby improving reliability in identifying critical conditions.
2Measurement precision
If machine learning models are used to detect critical conditions, then the detection capability improves, but the prioritization effectiveness deteriorates due to lack of uncertainty consideration in the sorting mechanism
Solution Approach 1:
The patent implements feedback by using the machine learning model's uncertainty output as input for the prioritization process. The uncertainty values generated by the model during detection are fed back into the sorting mechanism, creating a closed-loop system where detection quality directly informs prioritization decisions, thereby improving both detection capability and prioritization effectiveness.
Solution Approach 2:
The patent adds uncertainty as a critical parameter to the prioritization function. By modifying the sorting mechanism to consider statistical parameters including uncertainty alongside detection results, the system enhances prioritization effectiveness while maintaining the improved detection capability provided by the machine learning model.
3Speed
If images are sorted solely by detection results, then the processing speed is high, but the patient safety deteriorates due to potential delays in critical care for severe conditions with high uncertainty
Solution Approach 1:
The patent modifies the sorting parameters to include uncertainty measurements alongside detection results. By changing the prioritization from single-parameter (detection result only) to multi-parameter (detection result plus uncertainty), the system maintains high processing speed while preventing delays in critical care through more accurate risk assessment.
Solution Approach 2:
The patent performs preliminary uncertainty assessment as part of the detection process. By calculating statistical parameters including uncertainty before final prioritization, the system proactively identifies images that require urgent attention, preventing delays in critical care while maintaining efficient processing throughput.
Data Source
AI summary
A system and method for prioritizing a set of medical images to be evaluated using a machine learning model, including: training the machine learning model using a training data set, wherein the machine learning model receives input medical images and outputs a medical condition shown in the input medical images; running the trained machine learning model on the set of medical images to be evaluated to produce a medical condition output for each of the set of medical images; calculating a likelihood score for each medical condition outputs based upon a determined statistical parameters for the different outputs of the machine learning model; and determining the order of the set of input images to be evaluated based upon the calculated likelihood score and a severity of the medical condition outputs.