Medical Imaging Log Prioritization for Faster Fault Resolution
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Solution Overview
Problem
Medical imaging devices often malfunction, leading to significant downtime due to the time-consuming process of downloading and searching through large log files to diagnose faults, which can be exacerbated by bandwidth constraints in remote troubleshooting.
Innovation Solution
A method using machine learning to predict the likelihood that specific log files will assist in resolving device faults by analyzing user browsing history and providing prioritized log files for engineers, reducing unnecessary data retrieval and guiding an optimal browsing route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service engineers download and search through all log files to diagnose faults, then comprehensive diagnostic information is obtained, but significant time is lost and system downtime increases
Solution Approach 1:
The machine learning model performs preliminary analysis of log files before service engineers review them. By pre-processing and prioritizing log files based on predicted relevance to the fault, the system prepares diagnostic information in advance, allowing engineers to focus only on the most relevant files and significantly reducing their search time while maintaining diagnostic accuracy.
2Loss of information
If all log files are retrieved for fault diagnosis, then complete diagnostic data is available, but data transfer time and bandwidth consumption increase
Solution Approach 1:
The system extracts only the most relevant log files from the complete set of available logs using machine learning predictions. By identifying and separating the subset of log files with highest predicted relevance to the specific fault, the system transfers only necessary data to service engineers, reducing bandwidth consumption and retrieval time while preserving all essential diagnostic information.
3Reliability
If service engineers manually search through multiple log files, then thorough fault analysis is possible, but the complexity of the diagnostic process increases
Solution Approach 1:
The machine learning model acts as an intermediary between the raw log files and service engineers. It automatically analyzes and ranks log files based on their predicted relevance to the fault, presenting a pre-organized, prioritized list to engineers. This intermediary processing layer reduces the complexity of manual search while maintaining thorough diagnostic coverage.
4Productivity
If log files are prioritized using machine learning predictions, then relevant log files are identified faster, but additional processing time is required for model prediction
Solution Approach 1:
The machine learning model is trained in advance on historical log data and fault information, learning patterns that predict log file relevance. During actual fault diagnosis, the pre-trained model quickly scores and ranks available log files without requiring extensive processing time. The preliminary training phase invests computational resources upfront, enabling rapid predictions during critical fault resolution scenarios.
Data Source
AI summary
A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.