Prediction of MRI Hardware Failures based on Image Features using Time Series Classification
This work addresses the need to avoid unplanned downtime in medical imaging systems, which is crucial for patient health and healthcare operations, but it is incremental as it applies existing time series classification methods to a specific domain.
The paper tackled the problem of predicting MRI hardware failures by using image features and their variation over time to train a statistical model, achieving an F-score of 86.43% and 98.33% accuracy in determining if hardware should be replaced.
Already before systems malfunction one has to know if hardware components will fail in near future in order to counteract in time. Thus, unplanned downtime is ought to be avoided. In medical imaging, maximizing the system's uptime is crucial for patients' health and healthcare provider's daily business. We aim to predict failures of Head/Neck coils used in Magnetic Resonance Imaging (MRI) by training a statistical model on sequential data collected over time. As image features depend on the coil's condition, their deviations from the normal range already hint to future failure. Thus, we used image features and their variation over time to predict coil damage. After comparison of different time series classification methods we found Long Short Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with 98.33% accuracy if hardware should be replaced.