ArticleJournal of applied clinical medical physics2023
Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A glimpse into the CyberKnife robotic radiosurgery system: technical specifications and developments during thirty years.Frontiers in oncology · 2026Review
- Signal-aware deep learning-based respiratory motion prediction for lung tumor management.Frontiers in oncology · 2026Article
- Surface-guided breathing signal integration in breathing-adapted intelligent 4D computed tomography: prototype implementation and comparison with an infrared marker-based system.Physics and imaging in radiation oncology · 2025Article
- The effect of training data size on real-time respiration prediction using long short-term memory model.Radiation oncology (London, England) · 2025Article
- Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review.Radiation oncology (London, England) · 2024Review
- Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.Journal of applied clinical medical physics · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural networks to propose an accurate and reliable prediction model.
methodsSeven deep predictor models are trained and tested with 800 breathing signals. In this regard, a nonsequential-correlated hyperparameter optimization algorithm is developed to find the best configuration of parameters for all models. The root mean square error (RMSE), mean absolute error, normalized RMSE, and statistical F-test are also used to evaluate network performance.
resultsOverall, tuning the hyperparameters results in a 25%-30% improvement for all models compared to previous studies. The comparison between all models also shows that the gated recurrent unit (GRU) with RMSE = 0.108 ± 0.068 mm predicts respiratory signals with higher accuracy and better performance.
conclusionOverall, tuning the hyperparameters in the GRU model demonstrates a better result than the hybrid predictor model used in the CyberKnife VSI system to compensate for the 115 ms system latency. Additionally, it is demonstrated that the tuned parameters have a significant impact on the prediction accuracy of each model.
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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.