LSTM Neural Network for Medical Imaging Device Test Case Generation
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Solution Overview
Problem
Conventional testing methodologies for medical imaging devices are inadequate in capturing comprehensive and dynamic usage patterns, leading to potential device failures due to limited operational profiles and lack of integration with actual clinical workflows, which are not updated frequently enough to reflect advancing clinical practices.
Innovation Solution
A system that processes field log files to identify prominent usage patterns and predict future workflow patterns using a long short-term memory (LSTM) neural network, generating test scripts that reflect actual device usage and incorporating future variations, thereby enhancing the reliability of medical imaging devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing scenarios are included in operational profiles to capture all clinical workflows, then device reliability is improved, but testing time and cost increase significantly
Solution Approach 1:
The system performs preliminary analysis of field log files to identify prominent usage patterns before generating test scenarios. This allows the most critical workflows to be prioritized in the operational profile, ensuring comprehensive testing of high-risk scenarios without including every possible test case, thus reducing overall testing time while maintaining reliability.
Solution Approach 2:
The system uses LSTM neural networks to dynamically adjust test scenario parameters based on predicted future workflows. By changing the composition and weight of test scenarios according to predicted usage patterns, the system focuses testing resources on the most relevant scenarios, reducing unnecessary testing time while improving reliability through targeted testing.
2Reliability
If operational profiles are updated frequently to reflect advancing clinical practices, then testing comprehensiveness is improved, but resource consumption increases
Solution Approach 1:
The system continuously monitors field log files from actual device usage and uses this feedback to automatically update operational profiles. The LSTM model learns from ongoing clinical practices and adjusts test scenarios accordingly, ensuring comprehensiveness without manual intervention. This automated feedback loop reduces resource consumption by eliminating the need for manual profile updates while maintaining up-to-date testing coverage.
Solution Approach 2:
The system performs self-updating of operational profiles by automatically analyzing field log files and generating updated test scenarios using the LSTM model. This self-service capability ensures the operational profile remains comprehensive and current without requiring external resources for manual updates, reducing resource consumption while maintaining testing comprehensiveness.
3Measurement precision
If field expert knowledge is used to create operational profiles, then test scenario accuracy is improved, but adaptability to new clinical practices deteriorates
Solution Approach 1:
The system combines the strengths of both expert knowledge and machine learning by using the LSTM model to process field log files from diverse clinical settings. This universal approach captures actual usage patterns across different hospitals and practitioners, creating test scenarios that are both accurate (inheriting from expert-labeled data) and adaptable (learning from ongoing diverse clinical practices) without being limited to a single expert's knowledge base.
Data Source
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AI summary
Systems and methods are disclosed for generating device test cases for medical imaging devices, which are not only reflective of current actual field usage of the device but also provide outlook on future usage. A probabilistic model of usage patterns is generated from historic data present in the device field logs by mining the current usage patterns. A Deep Long Short Term Memory Neural Network model of the usage patterns is constructed to predict the future usage patterns. Additionally, to capture the changing trends of device usage patterns in the field, predictive models are continuously updated in real time, and the test cases generated by the models are integrated into an automated testing system.