RECURRENT NEURAL NETWORK FOR TUMOR MOVEMENT PREDICTION

In an embodiment, there is provided a method of predicting respiratory motion in real-time. The method includes training, by a training module, a recurrent neural network circuitry in real time. The training is performed over a training time interval. The training is based, at least in part, on a tr...

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Bibliographische Detailangaben
Hauptverfasser:	Hollingsworth, Alex, McDonald, Nathan, Huang, Zhaoran, Thiem, Clare, Liang, Zhizhuo
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA HUMAN NECESSITIES HYGIENE IDENTIFICATION INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY
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Beschreibung
Zusammenfassung:	In an embodiment, there is provided a method of predicting respiratory motion in real-time. The method includes training, by a training module, a recurrent neural network circuitry in real time. The training is performed over a training time interval. The training is based, at least in part, on a training data set. The training data set includes a training number of measured position samples. The measured position samples are related to respiratory motion of a patient target region. The method further includes predicting, by the trained recurrent neural network circuitry, a future position of the patient target region at a future point in time based, at least in part, on a prediction data set. The future point in time is a look ahead time interval in the future relative to a prediction time interval. The prediction data set includes a prediction number of measured position samples.