A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry | Zendy

James L. Bedford | Zendy; Ian Hanson | Zendy

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A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry

Author(s) -

James L. Bedford,

Ian Hanson

Publication year - 2022

Publication title -

physics and imaging in radiation oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.777

H-Index - 12

ISSN - 2405-6316

DOI - 10.1016/j.phro.2022.03.004

Subject(s) - artificial neural network , computer science , false positive paradox , artificial intelligence , false positives and false negatives , imaging phantom , dosimetry , pattern recognition (psychology) , nuclear medicine , medicine

Real-time portal dosimetry compares measured images with predicted images to detect delivery errors as the radiotherapy treatment proceeds. This work aimed to investigate the performance of a recurrent neural network for processing image metrics so as to detect delivery errors as early as possible in the treatment.

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