Medical Image Series Scheduling via Predictive Availability Analysis
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Solution Overview
Problem
Existing medical image analysis systems face inefficiencies in processing and resource utilization due to the unpredictable availability of medical image series and the need to determine whether to wait for additional series or process available ones, leading to suboptimal results and potential delays in clinical decision-making.
Innovation Solution
A computer-implemented method and system that predicts the availability and parameters of medical image series, selects a target series based on predicted time and performance, and feeds it into an image analysis machine learning model, optimizing resource utilization and processing by dynamically adapting weights and scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system waits for additional medical image series to become available, then the completeness and accuracy of analysis results is improved, but the processing time and resource utilization deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting future series availability and characteristics before actually waiting. It uses machine learning models to forecast when additional series will be available and what their parameters will be, allowing the system to make informed scheduling decisions in advance rather than passively waiting, thus reducing unnecessary delays while maintaining analysis quality
Solution Approach 2:
The series selection and scheduling process is made dynamic through continuous re-evaluation. The system dynamically adjusts the target series selection based on changing conditions, including newly available series, updated predictions, and varying resource availability. This dynamic approach allows the system to optimize between waiting for better data and processing available data in real-time
2Productivity
If the system processes available series immediately, then the productivity and resource utilization is improved, but the quality and completeness of analysis deteriorates
Solution Approach 1:
The system performs preliminary evaluation of available series using predicted parameters and characteristics before committing to processing. By forecasting the quality and relevance of upcoming series, the system can determine in advance whether waiting for additional series would improve analysis quality, thus making proactive quality assurance decisions that prevent premature processing of suboptimal data
Solution Approach 2:
The system implements feedback mechanisms where analysis results from processed series are continuously evaluated against predicted outcomes and quality thresholds. This feedback loop allows the system to learn from processing decisions and adjust future series selection and timing, improving the balance between productivity and quality over time through adaptive optimization
3Measurement precision
If the system selects target series based on multiple parameters and predictions, then the analysis accuracy is improved, but the system complexity increases
Solution Approach 1:
The system employs a universal machine learning framework that handles multiple series selection criteria and prediction tasks through integrated models. Rather than implementing separate complex systems for each function (prediction, evaluation, selection, scheduling), a multi-functional ML architecture performs all these tasks through unified algorithms, reducing overall system complexity while maintaining high analysis accuracy through comprehensive parameter consideration
Data Source
AI summary
There is provided a computer implemented method of scheduling analysis of at least one series of a study of medical images of a subject, comprising: predicting a time when at least one series of the study which is not yet available for processing, will be available for processing, predicting at least one parameter of the at least one series which is not yet available for processing, and obtaining the at least one parameter for series which are available, selecting a target series according to a combination of the predicted time and the at least one parameter, in response to the target series not yet available for processing, waiting for the target series to become available for processing, and in response to the target series being available for processing, feeding the target series into an image analysis machine learning model.