{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T18:52:12Z","timestamp":1785005532719,"version":"3.55.0"},"reference-count":17,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Patient\u2010specific organ\u2010dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on time\u2010consuming Monte Carlo methods or\/and generalized anthropomorphic phantoms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We proposed a proof\u2010of\u2010concept rapid workflow based on deep learning networks to estimate organ doses for individuals following thorax Computed Tomography (CT) examinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>CT scan data from 95 individuals undergoing thorax CT examinations were used. Monte Carlo simulations were performed and three\u2010dimensional (3D) dose distributions for each patient were obtained. A fully connected sequential deep learning network model was constructed and trained for each organ considered in this study. Water\u2010equivalent diameter (WED), scan length, and tube current were the independent variables. Organ doses for heart, lungs, esophagus, and bones were calculated from the Monte Carlo 3D distribution and used to train the deep learning networks. Organ dose predictions from each network were evaluated using an independent data set of 19 patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The trained networks provided organ dose predictions within a second. There was very good agreement between the deep learning network predictions and reference organ dose values calculated from Monte Carlo simulations. The average difference was \u22121.5% for heart, \u22121.6% for esophagus, \u22121.0% for lungs, and \u22120.4% for bones in the 95 patients dataset, and \u22125.1%, 4.3%, 0.9%, and 1.4% respectively in the 19 patients test dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed workflow demonstrated that patient\u2010specific organ\u2010doses can be estimated in nearly real\u2010time using deep learning networks. The workflow can be readily implemented and requires a small set of representative data for training.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16356","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T03:08:34Z","timestamp":1678849714000},"page":"7236-7244","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Rapid estimation of patient\u2010specific organ doses using a deep learning network"],"prefix":"10.1002","volume":"50","author":[{"given":"Marios","family":"Myronakis","sequence":"first","affiliation":[{"name":"Department of Medical Physics School of Medicine University of Crete  Iraklion Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Stratakis","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Medicine University of Crete  Iraklion Greece"},{"name":"Medical Physics Department University Hospital of Crete  Iraklion Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Damilakis","sequence":"additional","affiliation":[{"name":"Department of Medical Physics School of Medicine University of Crete  Iraklion Greece"},{"name":"Medical Physics Department University Hospital of Crete  Iraklion Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000727"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/3\/R129"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.262055138"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13625"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4711748"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2896075"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/50\/17\/005"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15488"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08756-w"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab65dc"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2019"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1978.4310039"},{"issue":"220","key":"e_1_2_8_14_1","first-page":"6","article-title":"Use of water equivalent diameter for calculating patient size and size\u2010specific dose estimates (SSDE) in CT: the report of AAPM task group 220","author":"McCollough C","year":"2014","journal-title":"AAPM Rep"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-007-0815-7"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/rpd\/ncp051"},{"key":"e_1_2_8_17_1","unstructured":"The Mathworks I.Deep Learning Toolbox: Natick Massachusetts United States.2011.https:\/\/www.mathworks.com\/help\/deeplearning\/"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017152852"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16356","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.16356","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16356","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T17:49:31Z","timestamp":1699379371000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":17,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.1002\/mp.16356"],"URL":"https:\/\/doi.org\/10.1002\/mp.16356","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,23]]},"assertion":[{"value":"2022-08-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}