{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:07:37Z","timestamp":1767373657698,"version":"3.40.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004484","name":"Tehran University of Medical Sciences and Health Services","doi-asserted-by":"publisher","award":["54042"],"award-info":[{"award-number":["54042"]}],"id":[{"id":"10.13039\/501100004484","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s13246-023-01276-x","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:01:11Z","timestamp":1685286071000},"page":"1071-1080","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS)"],"prefix":"10.1007","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2516-2765","authenticated-orcid":false,"given":"Bahareh Moradmand","family":"Bahonar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2015-1642","authenticated-orcid":false,"given":"Vahid","family":"Changizi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Ebrahiminia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samaneh","family":"Baradaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"1276_CR1","doi-asserted-by":"publisher","DOI":"10.5041\/rmmj.10355","author":"E Bercovich","year":"2018","unstructured":"Bercovich E, Javitt MC (2018) Medical imaging: from roentgen to the digital revolution, and beyond. Rambam Maimonides Med J. https:\/\/doi.org\/10.5041\/rmmj.10355","journal-title":"Rambam Maimonides Med J"},{"issue":"2","key":"1276_CR2","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1148\/radiol.2532082010","volume":"253","author":"FA Mettler Jr","year":"2009","unstructured":"Mettler Jr FA, Bhargavan M, Faulkner K, Gilley DB, Gray JE, Ibbott GS, \u2026, Yoshizumi TT (2009) Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources\u20141950\u20132007. Radiology 253(2):520\u2013531. https:\/\/doi.org\/10.1148\/radiol.2532082010","journal-title":"Radiology"},{"key":"1276_CR3","doi-asserted-by":"publisher","unstructured":"Huppmann MV, Johnson WB, Javitt MC (2010) Radiation risks from exposure to chest computed tomography. In: Seminars in Ultrasound, CT and MRI (Vol.\u00a031, No. 1, pp.\u00a014\u201328). WB Saunders. https:\/\/doi.org\/10.1053\/j.sult.2009.09.003","DOI":"10.1053\/j.sult.2009.09.003"},{"key":"1276_CR4","doi-asserted-by":"publisher","DOI":"10.1097\/md.0000000000026692","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Zheng Y, Wen Y, Dai X, Liu W, Gong Q, \u2026, Wu J (2021) Radiation dose levels in chest computed tomography scans of coronavirus disease 2019 pneumonia: a survey of 2119 patients in Chongqing, southwest China. Medicine. https:\/\/doi.org\/10.1097\/md.0000000000026692","journal-title":"Medicine"},{"issue":"1","key":"1276_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40134-013-0007-y","volume":"1","author":"Y Thakur","year":"2013","unstructured":"Thakur Y, McLaughlin PD, Mayo JR (2013) Strategies for radiation dose optimization. Curr Radiol Rep 1(1):1\u201310. https:\/\/doi.org\/10.1007\/s40134-013-0007-y","journal-title":"Curr Radiol Rep"},{"issue":"1","key":"1276_CR6","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1148\/radiol.2511081296","volume":"251","author":"A Sodickson","year":"2009","unstructured":"Sodickson A, Baeyens PF, Andriole KP, Prevedello LM, Nawfel RD, Hanson R, Khorasani R (2009) Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. Radiology 251(1):175. https:\/\/doi.org\/10.1148\/radiol.2511081296","journal-title":"Radiology"},{"issue":"3","key":"1276_CR7","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1258\/jms.2008.008052","volume":"15","author":"A Berrington de Gonz\u00e1lez","year":"2008","unstructured":"Berrington de Gonz\u00e1lez A, Kim KP, Berg CD (2008) Low-dose lung computed tomography screening before age 55: estimates of the mortality reduction required to outweigh the radiation-induced cancer risk. J Med Screen 15(3):153\u2013158. https:\/\/doi.org\/10.1258\/jms.2008.008052","journal-title":"J Med Screen"},{"issue":"1","key":"1276_CR8","doi-asserted-by":"publisher","first-page":"W81","DOI":"10.2214\/ajr.12.9226","volume":"201","author":"JM Albert","year":"2013","unstructured":"Albert JM (2013) Radiation risk from CT: implications for cancer screening. Am J Roentgenol 201(1):W81\u2013W87. https:\/\/doi.org\/10.2214\/ajr.12.9226","journal-title":"Am J Roentgenol"},{"issue":"24","key":"1276_CR9","first-page":"2","volume":"37","author":"R Protection","year":"2007","unstructured":"Protection R (2007) ICRP publication 103. Ann ICRP 37(24):2","journal-title":"Ann ICRP"},{"issue":"1","key":"1276_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ejrad.2010.06.032","volume":"76","author":"A Meghzifene","year":"2010","unstructured":"Meghzifene A, Dance DR, McLean D, Kramer HM (2010) Dosimetry in diagnostic radiology. Eur J Radiol 76(1):11\u201314. https:\/\/doi.org\/10.1016\/j.ejrad.2010.06.032","journal-title":"Eur J Radiol"},{"issue":"1","key":"1276_CR11","doi-asserted-by":"publisher","first-page":"64","DOI":"10.22038\/ijmp.2018.29348.1315","volume":"16","author":"P Kalhor","year":"2019","unstructured":"Kalhor P, Changizi V, Hosseini SA, Jazayeri E (2019) Optimization of Head CT Protocol to reduce the absorbed dose in Eye lenses and thyroid: a Phantom Study. Iran J Med Phys 16(1):64\u201374. https:\/\/doi.org\/10.22038\/ijmp.2018.29348.1315","journal-title":"Iran J Med Phys"},{"issue":"1011","key":"1276_CR12","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1259\/bjr\/22285164","volume":"85","author":"RJ Loader","year":"2012","unstructured":"Loader RJ, Gosling O, Roobottom C, Morgan-Hughes G, Rowles N (2012) Practical dosimetry methods for the determination of effective skin and breast dose for a modern CT system, incorporating partial irradiation and prospective cardiac gating. Br J Radiol 85(1011):237\u2013248. https:\/\/doi.org\/10.1259\/bjr\/22285164","journal-title":"Br J Radiol"},{"key":"1276_CR13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1159\/000488198","volume":"27","author":"K Alkhalifah","year":"2018","unstructured":"Alkhalifah K, Brindhaban A (2018) Investigation of exposure factors for various breast composition and thicknesses in digital screening mammography related to breast dose. Med Principles Pract 27:211\u2013216. https:\/\/doi.org\/10.1159\/000488198","journal-title":"Med Principles Pract"},{"key":"1276_CR14","unstructured":"Shrimpton PC, Hillier MC, Meeson S, Golding SJ (2014) Doses from computed tomography (CT) examinations in the UK-2011 review. Public Health England.\u00a0\u00a0https:\/\/www.gov.uk\/government\/uploads\/system\/uploads\/attachment_data\/file\/349188\/PHE_CRCE_013.pdf"},{"issue":"3","key":"1276_CR15","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1148\/radiol.11111055","volume":"261","author":"R Smith-Bindman","year":"2011","unstructured":"Smith-Bindman R, Miglioretti DL (2011) CTDIvol, DLP, and effective dose are excellent measures for use in CT quality improvement. Radiology 261(3):999. https:\/\/doi.org\/10.1148\/radiol.11111055","journal-title":"Radiology"},{"issue":"4","key":"1276_CR16","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1093\/rpd\/ncaa073","volume":"F189","author":"A Ebrahiminia","year":"2020","unstructured":"Ebrahiminia A, Asadinezhad M, Mohammadi F, Khoshgard K (2020) Eye lens dose optimization through gantry tilting in brain ct scan: the potential effect of the radiological technologists\u2019 training. Radiat Prot Dosim F189(4):527\u2013533. https:\/\/doi.org\/10.1093\/rpd\/ncaa073","journal-title":"Radiat Prot Dosim"},{"key":"1276_CR17","unstructured":"American Association of Physicists in Medicine (2011) Size-Specific dose estimates (SSDE) in pediatric and adult body CT exam- inations. Report of AAPM Task Group 204."},{"issue":"220","key":"1276_CR18","first-page":"1","volume":"220","author":"AAPM","year":"2014","unstructured":"AAPM, \u201cAAPM TG 220 (2014) Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT. AAPM Rep 220(220):1\u201323","journal-title":"AAPM Rep"},{"issue":"11","key":"1276_CR19","doi-asserted-by":"publisher","first-page":"6764","DOI":"10.1118\/1.4754303","volume":"39","author":"J Wang","year":"2012","unstructured":"Wang J, Duan X, Christner JA, Leng S, Yu L, McCollough CH (2012) Attenuation-based estimation of patient size for the purpose of size specific dose estimation in CT. Part I. Development and validation of methods using the CT image. Med Phys 39(11):6764\u20136771. https:\/\/doi.org\/10.1118\/1.4754303","journal-title":"Med Phys"},{"issue":"7","key":"1276_CR20","doi-asserted-by":"publisher","first-page":"20170047","DOI":"10.1259\/dmfr.20170047","volume":"46","author":"M Ernst","year":"2017","unstructured":"Ernst M, Manser P, Dula K, Volken W, Stampanoni MF, Fix MK (2017) TLD measurements and Monte Carlo calculations of head and neck organ and effective doses for cone beam computed tomography using 3D accuitomo 170. Dentomaxillofacial Radiol 46(7):20170047. https:\/\/doi.org\/10.1259\/dmfr.20170047","journal-title":"Dentomaxillofacial Radiol"},{"issue":"920","key":"1276_CR21","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1259\/bjr\/48307881","volume":"77","author":"AM Groves","year":"2004","unstructured":"Groves AM, Owen KE, Courtney HM, Yates SJ, Goldstone KE, Blake GM, Dixon AK (2004) 16-detector multislice CT: dosimetry estimation by TLD measurement compared with Monte Carlo simulation. Br J Radiol 77(920):662\u2013665. https:\/\/doi.org\/10.1259\/bjr\/48307881","journal-title":"Br J Radiol"},{"issue":"2","key":"1276_CR22","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1088\/1361-6498\/aac336","volume":"38","author":"S Gharbi","year":"2018","unstructured":"Gharbi S, Labidi S, Mars M, Chelli M, Meftah S, Ladeb MF (2018) Assessment of organ dose and image quality in head and chest CT examinations: a phantom study. J Radiol Prot 38(2):807. https:\/\/doi.org\/10.1088\/1361-6498\/aac336","journal-title":"J Radiol Prot"},{"key":"1276_CR23","unstructured":"Hashemi-Malayeri B, Williams JR (2003) A practical approach for the assessment of patient doses from CT examinations. Radiography, 64: 35.\u00a0\u00a0www.dundee.ac.uk\/medphys\/documents\/hashemi.pdf"},{"issue":"917","key":"1276_CR24","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1259\/bjr\/91138314","volume":"77","author":"HM Warren-Forward","year":"2004","unstructured":"Warren-Forward HM, Duggan L (2004) Towards in vivo TLD dosimetry in mammography. Br J Radiol 77(917):426\u2013432. https:\/\/doi.org\/10.1259\/bjr\/91138314","journal-title":"Br J Radiol"},{"issue":"1\u20134","key":"1276_CR25","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1093\/oxfordjournals.rpd.a032865","volume":"85","author":"T Kron","year":"1999","unstructured":"Kron T (1999) Applications of thermoluminescence dosimetry in medicine. Radiat Prot Dosimetry 85(1\u20134):333\u2013340. https:\/\/doi.org\/10.1093\/oxfordjournals.rpd.a032865","journal-title":"Radiat Prot Dosimetry"},{"issue":"3\u20136","key":"1276_CR26","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.radmeas.2010.01.016","volume":"45","author":"P Olko","year":"2010","unstructured":"Olko P (2010) Advantages and disadvantages of luminescence dosimetry. Radiat Meas 45(3\u20136):506\u2013511. https:\/\/doi.org\/10.1016\/j.radmeas.2010.01.016","journal-title":"Radiat Meas"},{"key":"1276_CR27","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/sj.bdj.4812743","volume":"199","author":"YK Tu","year":"2005","unstructured":"Tu YK, Kellett M, Clerehugh V et al (2005) Problems of correlations between explanatory variables in multiple regression analyses in the dental literature. Br Dent J 199:457\u2013461. https:\/\/doi.org\/10.1038\/sj.bdj.4812743","journal-title":"Br Dent J"},{"key":"1276_CR28","first-page":"163","volume":"13","author":"T Erguzel","year":"2018","unstructured":"Erguzel T, Tekin HO, Manici Tu\u011fbaAltunsoy Guclu, Elif Tarhan K (2018) Comparison of multiple linear regression analysis and artificial neural network approaches in the estimation of Monte Carlo mean glandular dose calculations of mammography. Dig J Nanomater Biostruct 13:163\u2013176","journal-title":"Dig J Nanomater Biostruct"},{"issue":"1","key":"1276_CR29","doi-asserted-by":"publisher","first-page":"8117","DOI":"10.1038\/s41598-020-65121-x","volume":"10","author":"S Kalantary","year":"2020","unstructured":"Kalantary S, Jahani A, Jahani R (2020) MLR and ANN approaches for prediction of synthetic\/natural nanofibers diameter in the environmental and medical applications. Sci Rep 10(1):8117. https:\/\/doi.org\/10.1038\/s41598-020-65121-x","journal-title":"Sci Rep"},{"issue":"7","key":"1276_CR30","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.3390\/children9071044","volume":"9","author":"CK Ng","year":"2022","unstructured":"Ng CK (2022) Artificial Intelligence for Radiation Dose optimization in Pediatric Radiology. Syst Rev Child 9(7):1044. https:\/\/doi.org\/10.3390\/children9071044","journal-title":"Syst Rev Child"},{"issue":"3","key":"1276_CR31","doi-asserted-by":"publisher","first-page":"035007","DOI":"10.1088\/1361-6560\/ab65dc","volume":"65","author":"TI G\u00f6tz","year":"2020","unstructured":"G\u00f6tz TI, Schmidkonz C, Chen S, Al-Baddai S, Kuwert T, Lang EW (2020) A deep learning approach to radiation dose estimation. Phys Med Biol 65(3):035007. https:\/\/doi.org\/10.1088\/1361-6560\/ab65dc","journal-title":"Phys Med Biol"},{"issue":"2","key":"1276_CR32","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1148\/radiol.2018171820","volume":"288","author":"G Choy","year":"2018","unstructured":"Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Dreyer KJ (2018) Current applications and future impact of machine learning in radiology. Radiology 288(2):318. https:\/\/doi.org\/10.1148\/radiol.2018171820","journal-title":"Radiology"},{"key":"1276_CR33","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2015905635","author":"N Walia","year":"2015","unstructured":"Walia N, Singh H, Sharma A (2015) ANFIS: adaptive neuro-fuzzy inference system-a survey. Int J Comput Appl. https:\/\/doi.org\/10.5120\/ijca2015905635","journal-title":"Int J Comput Appl"},{"issue":"10","key":"1276_CR34","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","volume":"42","author":"JSR Jang","year":"1997","unstructured":"Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans Autom Control 42(10):1482\u20131484","journal-title":"IEEE Trans Autom Control"},{"key":"1276_CR35","unstructured":"IAEA (2012) Quality assurance programme for computed tomography: diagnostic and therapy applications. IAEA Human Health Series.\u00a0\u00a0http:\/\/www-pub.iaea.org\/MTCD\/publications\/PDF\/Pub1557_web.pdf"},{"issue":"2","key":"1276_CR36","doi-asserted-by":"publisher","first-page":"367","DOI":"10.2214\/AJR.10.4569","volume":"197","author":"SJ Foley","year":"2011","unstructured":"Foley SJ, McEntee MF, Achenbach S, Brennan PC, Rainford LS, Dodd JD (2011) Breast surface radiation dose during coronary CT angiography: reduction by breast displacement and lead shielding. AJR-Am J Roentgenol 197(2):367. https:\/\/doi.org\/10.2214\/AJR.10.4569","journal-title":"AJR-Am J Roentgenol"},{"issue":"3","key":"1276_CR37","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JS Jang","year":"1993","unstructured":"Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665\u2013685. https:\/\/doi.org\/10.1109\/21.256541","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"1276_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2016.08.007","author":"S Ausati","year":"2016","unstructured":"Ausati S, Amanollahi J (2016) Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5. Atmos Environ. https:\/\/doi.org\/10.1016\/j.atmosenv.2016.08.007","journal-title":"Atmos Environ"},{"key":"1276_CR39","doi-asserted-by":"crossref","unstructured":"Kawaguchi A, Kobayashi M, Suzuki M, Otsuka T, Hattori S, Suzuki S (2012) Average Glandular Dose and Entrance Surface Dose in Mammography. Int Radiat Prot Assoc P07.129, pp 1\u20137. https:\/\/www.irpa.net\/members\/P07.129fp.pdf","DOI":"10.1007\/s12194-013-0239-9"},{"key":"1276_CR40","unstructured":"Desterro FS, Almeida AA, Pereira CM (2017) Improvement of radiation dose estimation due to nuclear accidents using deep neural network and Gpu. Ann INAC.\u00a0https:\/\/inis.iaea.org\/collection\/NCLCollectionStore\/_Public\/49\/066\/49066552.pdf?r=1"},{"key":"1276_CR41","doi-asserted-by":"publisher","unstructured":"Pereira CMDNA, Schirru R, Gomes K, Cunha JL (2016) Artificial neural networks for radiation dose prediction in nuclear emergencies-preliminary investigations. In 2016 International conference on modeling, simulation and optimization technologies and applications (MSOTA2016). Atlantis Press,\u00a0pp 441\u2013444.\u00a0https:\/\/doi.org\/10.2991\/msota-16.2016.98","DOI":"10.2991\/msota-16.2016.98"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01276-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-023-01276-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01276-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T20:53:06Z","timestamp":1744145586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-023-01276-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,28]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1276"],"URL":"https:\/\/doi.org\/10.1007\/s13246-023-01276-x","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"type":"print","value":"2662-4729"},{"type":"electronic","value":"2662-4737"}],"subject":[],"published":{"date-parts":[[2023,5,28]]},"assertion":[{"value":"21 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. The Approval was granted by the ethics committee of Tehran University of Medical Science, Iran [IR.TUMS.SPH.REC.1399.219].","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}