Medical Scanner Fault Prediction from Images Without Internal Sensors
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Solution Overview
Problem
Existing technologies fail to effectively address the challenge of predicting and addressing scanner faults in legacy medical imaging devices that lack internal operability sensors, leading to potential image degradation and unplanned downtime.
Innovation Solution
Implement a cascaded deep learning pipeline comprising three neural networks to analyze captured medical images, predicting failure modes, identifying root causes, and estimating remaining useful life of hardware components, thereby facilitating proactive maintenance without relying on internal sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If internal operability sensors are installed in medical imaging scanners to predict failures, then scanner fault prediction accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary deep learning system that processes medical images to predict scanner failures. Instead of installing sensors directly in the scanner, the system uses existing medical images as intermediaries to infer scanner health status. The deep learning model acts as a mediator between the scanner's operational data (captured in images) and failure prediction, avoiding direct modification of the scanner hardware.
Solution Approach 2:
The patent creates a digital copy or virtual representation of the scanner's operational state through medical images. Rather than physically installing sensors to monitor scanner components, the system captures and analyzes image data that reflects the scanner's operational condition. This copying approach allows failure prediction without adding physical sensing components to the original device.
2Reliability
If internal operability sensors are retrofitted to legacy medical imaging scanners, then fault detection capability is improved, but ease of manufacture and deployment deteriorates
Solution Approach 1:
The patent enables legacy scanners to self-report their health status through existing medical images without requiring external sensor installations. The deep learning system processes images produced by the scanner itself, allowing the scanner to effectively monitor its own operational condition using data it already generates. This self-service approach eliminates the need for retroffitting sensors to legacy devices.
Solution Approach 2:
The patent makes the deep learning-based fault prediction system universally applicable to both legacy and modern scanners. By using medical images as the common data source, the system can analyze scanner health across different generations of equipment without requiring sensor retrofits. The same image-processing approach works universally across diverse scanner models and ages.
3Loss of time
If deep learning neural networks are executed on medical images for failure prediction, then scanner maintenance timing is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent applies partial action by selectively analyzing only the necessary features in medical images for failure prediction. Rather than processing entire images at full resolution continuously, the deep learning system focuses on specific regions and features that indicate scanner health status. This partial processing approach reduces computational energy consumption while maintaining effective fault detection timing.
4Adaptability or versatility
If image-based deep learning is used for fault prediction in legacy scanners, then adaptability to legacy devices is improved, but measurement precision of scanner operability deteriorates
Solution Approach 1:
The patent changes the measurement parameters from direct sensor readings to image-based features. Instead of measuring scanner operability through physical sensor data, the system transforms the measurement approach to extract operational indicators from medical image characteristics. This parameter transformation enables compatibility with legacy scanners while maintaining measurement capability through alternative image-based metrics.
Data Source
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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 (e.g., via 106) captured by a medical imaging scanner (e.g., 104). In various aspects, the system can generate, via execution of at least one of one or more deep learning neural networks (e.g., 302) on the medical image, a failure classification label (e.g., 304) that indicates that the medical imaging scanner is afflicted by a first defined scanning failure from a plurality of defined scanning failures (e.g., 402). In various instances, the system can transmit an electronic notification (e.g., 1002) 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.