{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:04:13Z","timestamp":1791893053600,"version":"4.3.4"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"1102","license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:sec>\n                  <jats:title>Objective:<\/jats:title>\n                  <jats:p>The accuracy of dose delivery for intensity modulated radiotherapy (IMRT) treatments should be determined by an accurate quality assurance procedure. In this work, we used artificial neural networks (ANNs) as an application for the pre-treatment dose verification of IMRT fields based two-dimensional-fluence maps acquired by an electronic portal imaging device (EPID).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>The ANN must be trained and validated before use for the pretreatment dose verification. Hence, 60 EPID fluence maps of the anteroposterior prostate and nasopharynx IMRT fields were used as an input for the ANN (feed forward type), and a dose map of those fluence maps that were acquired by two-dimensional Array Seven29TM as an output for the ANN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>After the training and validation of the neural network, the analysis of 20 IMRT anteroposterior fields showed excellent agreement between the ANN output and the dose map predicted by the treatment planning system. The average overall global and local \u03b3 field pass rate was greater than 90% for the prostate and nasopharynx fields, with the 2 mm\/3% criteria.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>The results indicated that the ANN can be used as a fast and powerful tool for pretreatment dose verification, based on an EPID fluence map.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>In this study, ANN is proposed for EPID based dose validation of IMRT fields. The proposed method has good accuracy and high speed in response to problems. Neural network show to be low price and precise method for IMRT fields verification<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjr.20190355","type":"journal-article","created":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T10:26:22Z","timestamp":1563445582000},"source":"Crossref","is-referenced-by-count":28,"title":["Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields"],"prefix":"10.1093","volume":"92","author":[{"given":"Seied Rabie","family":"Mahdavi","sequence":"first","affiliation":[{"name":"Department of Medical Physics, School of Medicine, Iran University of Medical Sciences , Tehran , Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asieh","family":"Tavakol","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Roshana Cancer Institute , Tehran , 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