Medical Scanner Fault Prediction From Image Degradation Patterns
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Solution Overview
Problem
Legacy medical imaging scanners lack internal operability sensors, making it difficult to forecast failures or malfunctions that affect image quality, as existing techniques rely on these sensors for monitoring hardware components.
Innovation Solution
Implement a cascaded deep learning pipeline comprising three neural networks to analyze captured medical images, predicting failure modes, root causes, and remaining useful life of hardware components without relying on internal sensors, using image-based deep learning to detect unique visual fingerprints of scanner malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If internal operability sensors are installed in legacy scanners, then failure monitoring capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses medical images as an intermediary carrier to transmit hardware failure information. Instead of directly monitoring hardware sensors, the system analyzes visual artifacts in medical images that indirectly reflect scanner component degradation, enabling failure detection without additional sensors in legacy scanners
Solution Approach 2:
The patent replaces the mechanical/sensor-based monitoring system with an image-based deep learning system. Instead of using physical sensors to detect hardware failures, the system uses neural networks to analyze visual patterns in medical images, substituting a complex sensor infrastructure with a software-based AI approach
2Measurement precision
If deep learning image analysis is implemented, then failure detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-training deep learning models on large datasets of medical images with known failure patterns. The models are trained in advance to recognize specific visual artifacts associated with hardware failures, enabling fast inference during actual scanner operation without requiring heavy real-time computation
Solution Approach 2:
The patent changes the parameter of image processing by using deep learning neural networks that can process medical images efficiently. The system optimizes model architecture and training parameters to achieve high detection accuracy while controlling computational resource consumption, using techniques like transfer learning and model pruning
Data Source
AI summary
Systems/techniques that facilitate scanner fault prediction via image-based deep learning are provided. In various embodiments, a system can access a medical image captured by a medical imaging scanner. In various aspects, the system can generate, via execution of at least one of one or more deep learning neural networks on the medical image, a failure classification label that indicates that the medical imaging scanner is afflicted by a first defined scanning failure from a plurality of defined scanning failures. In various instances, the system can transmit an electronic notification to a computing device associated with a technician of the medical imaging scanner, wherein the electronic notification can request that the medical imaging scanner be serviced to remedy the first defined scanning failure.