ArticleJournal of applied clinical medical physics2022
Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.Journal of imaging informatics in medicine · 2026Article
- Noninvasive prediction of photon fluence rate in targeted brain regions for personalized transcranial photobiomodulation dosage: model development andBiomedical optics express · 2025Article
- A pathway away from measurement-based patient-specific QA: A review of contemporary quality assurance for external-beam radiotherapy sub-systems.Journal of applied clinical medical physics · 2025Review
- Evaluation of the TrueBeam machine-performance-check (MPC): Collimator device check (CDC).Journal of applied clinical medical physics · 2025Article
- Review
- Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea.Scientific reports · 2023Article
- Predicting machine's performance record using the stacked long short-term memory (LSTM) neural networks.Journal of applied clinical medical physics · 2022Article
- Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and trend of data variations quantitively. METHODS AND MATERIALS: The record of 24 QC items for a radiotherapy machine was investigated in our institute. The QC records were collected daily for 3 years. The stacked long short-term memory (LSTM) model was used to develop the neural network model. A total of 867 records were collected to predict the record for the next 5 days. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. The accuracy of the model was quantified by the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R
resultsThe mean MAE, RMSE, and
conclusionsIn this study, the stacked LSTM model can accurately predict the record and trend of QC items. Moreover, the stacked LSTM model is robust when applied to another radiotherapy machine. Predicting future performance record will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.