{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:56:30Z","timestamp":1740135390061,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008532","name":"Universidade Federal do Rio Grande do Norte","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008532","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-1062-y","type":"journal-article","created":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T19:02:51Z","timestamp":1583175771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Design and clinical validation of a software program for automated measurement of mammographic breast density"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7486-1119","authenticated-orcid":false,"given":"Adriano L. C.","family":"Ara\u00fajo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heliana B.","family":"Soares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel F.","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto M.","family":"Mendon\u00e7a","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio G.","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"1062_CR1","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.1002\/1097-0142(197605)37:5<2486:AID-CNCR2820370542>3.0.CO;2-8","volume":"37","author":"JN Wolfe","year":"1976","unstructured":"Wolfe JN. Risk factors for breast Cancer development determined by mammographic parenchymal pattern. Cancer. 1976;37:2486\u201392. https:\/\/doi.org\/10.1002\/1097-0142(197605)37:5<2486:AID-CNCR2820370542>3.0.CO;2-8.","journal-title":"Cancer"},{"key":"1062_CR2","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.mcna.2017.03.005","volume":"101","author":"CI Lee","year":"2017","unstructured":"Lee CI, Chen LE, Elmore JG. Risk-based breast Cancer screening: implications of breast density. Med Clin North Am. 2017;101:725\u201341. https:\/\/doi.org\/10.1016\/j.mcna.2017.03.005.","journal-title":"Med Clin North Am"},{"key":"1062_CR3","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1093\/jnci\/87.9.670","volume":"87","author":"NF Boyd","year":"1995","unstructured":"Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, Lockwood GA, Tritchler DL, Yaffe MJ. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst. 1995;87:670\u20135. https:\/\/doi.org\/10.1093\/jnci\/87.9.670.","journal-title":"J Natl Cancer Inst"},{"key":"1062_CR4","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/bcr2942","volume":"10","author":"NF Boyd","year":"1998","unstructured":"Boyd NF, Lockwood GA, Martin LJ, Knight JA, Byng JW, Yaffe MJ, Tritchler DL. Mammographic densities and breast cancer risk. Breast Dis. 1998;10:113\u201326. https:\/\/doi.org\/10.1186\/bcr2942.","journal-title":"Breast Dis"},{"key":"1062_CR5","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1093\/jnci\/djk066","volume":"99","author":"K Kerlikowske","year":"2007","unstructured":"Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DSM, Vacek PM, Smith-Bindman R, Yankaskas B, Carney PA, Ballard-Barbash R. Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst. 2007;99:386\u201395. https:\/\/doi.org\/10.1093\/jnci\/djk066.","journal-title":"J Natl Cancer Inst"},{"key":"1062_CR6","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s10549-014-2901-2","volume":"144","author":"CW Huo","year":"2014","unstructured":"Huo CW, Chew GL, Britt KL, Ingman WV, Henderson MA, Hopper JL, Thompson EW. Mammographic density - a review on the current understanding of its association with breast cancer. Breast Cancer Res Treat. 2014;144:479\u2013502. https:\/\/doi.org\/10.1007\/s10549-014-2901-2.","journal-title":"Breast Cancer Res Treat"},{"key":"1062_CR7","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1186\/bcr1829","volume":"9","author":"CM Vachon","year":"2007","unstructured":"Vachon CM, van Gils CH, Sellers TA, Ghosh K, Pruthi S, Brandt KR, Pankratz VS. Mammographic density, breast cancer risk and risk prediction. Breast Cancer Res. 2007;9:217. https:\/\/doi.org\/10.1186\/bcr1829.","journal-title":"Breast Cancer Res"},{"key":"1062_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s13058-016-0701-9","volume":"18","author":"MJ Sherratt","year":"2016","unstructured":"Sherratt MJ, McConnell JC, Streuli CH. Raised mammographic density: causative mechanisms and biological consequences. Breast Cancer Res. 2016;18:45. https:\/\/doi.org\/10.1186\/s13058-016-0701-9.","journal-title":"Breast Cancer Res"},{"key":"1062_CR9","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/j.rcl.2010.06.012","volume":"48","author":"EA Sickles","year":"2010","unstructured":"Sickles EA. The use of breast imaging to screen women at high risk for cancer. Radiol Clin N Am. 2010;48:859\u201378. https:\/\/doi.org\/10.1016\/j.rcl.2010.06.012.","journal-title":"Radiol Clin N Am"},{"key":"1062_CR10","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1148\/radiographics.18.6.9821201","volume":"18","author":"JW Byng","year":"1998","unstructured":"Byng JW, Yaffe MJ, Jong RA, Shumak RS, Lockwood GA, Tritchler DL, Boyd NF. Analysis of mammographic density and breast Cancer risk from digitized. Radiographics. 1998;18:1587\u201398. https:\/\/doi.org\/10.1148\/radiographics.18.6.9821201.","journal-title":"Radiographics"},{"key":"1062_CR11","doi-asserted-by":"publisher","first-page":"673","DOI":"10.7326\/M14-1465","volume":"162","author":"K Kerlikowske","year":"2015","unstructured":"Kerlikowske K, Zhu W, Tosteson ANA, Sprague BL, Tice JA, Lehman CD, Miglioretti DL. Identifying women with dense breasts at high risk for interval cancer a cohort study. Ann Intern Med. 2015;162:673\u201381. https:\/\/doi.org\/10.7326\/M14-1465.","journal-title":"Ann Intern Med"},{"key":"1062_CR12","doi-asserted-by":"publisher","first-page":"2893","DOI":"10.1002\/ijc.25516","volume":"127","author":"J Ferlay","year":"2010","unstructured":"Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893\u2013917. https:\/\/doi.org\/10.1002\/ijc.25516.","journal-title":"Int J Cancer"},{"key":"1062_CR13","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.soc.2014.03.011","volume":"23","author":"KA Ban","year":"2014","unstructured":"Ban KA, Godellas CV. Epidemiology of breast Cancer. Surg Oncol Clin N Am. 2014;23:409\u201322. https:\/\/doi.org\/10.1016\/j.soc.2014.03.011.","journal-title":"Surg Oncol Clin N Am"},{"key":"1062_CR14","doi-asserted-by":"publisher","unstructured":"Pettersson A, Graff RE, Ursin G, Dos Santos SI, McCormack V, Baglietto L, et al. Mammographic density phenotypes and risk of breast cancer: A meta-analysis. J Natl Cancer Inst. 2014;106(5). https:\/\/doi.org\/10.1093\/jnci\/dju078.","DOI":"10.1093\/jnci\/dju078"},{"key":"1062_CR15","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1186\/s13058-014-0446-2","volume":"16","author":"A Howell","year":"2014","unstructured":"Howell A, Anderson AS, Clarke RB, Duffy SW, Evans DG, Garcia-Closas M, Gescher AJ, Key TJ, Saxton JM, Harvie MN. Risk determination and prevention of breast cancer. Breast Cancer Res. 2014;16:446. https:\/\/doi.org\/10.1186\/s13058-014-0446-2.","journal-title":"Breast Cancer Res"},{"issue":"2","key":"1062_CR16","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10549-005-5152-4","volume":"94","author":"JA Tice","year":"2005","unstructured":"Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005;94(2):115\u201322. https:\/\/doi.org\/10.1007\/s10549-005-5152-4.","journal-title":"Breast Cancer Res Treat"},{"key":"1062_CR17","doi-asserted-by":"publisher","first-page":"337","DOI":"10.7326\/0003-4819-148-5-200803040-00004","volume":"148","author":"JA Tice","year":"2008","unstructured":"Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148:337\u201347. https:\/\/doi.org\/10.7326\/0003-4819-148-5-200803040-00004.","journal-title":"Ann Intern Med"},{"key":"1062_CR18","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1093\/jnci\/djj331","volume":"98","author":"WE Barlow","year":"2006","unstructured":"Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DS, Geller BM, Rosenberg R, Yankaskas BC, Kerlikowske K. Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006;98:1204\u201314. https:\/\/doi.org\/10.1093\/jnci\/djj331.","journal-title":"J Natl Cancer Inst"},{"key":"1062_CR19","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1093\/jnci\/djj332","volume":"98","author":"J Chen","year":"2006","unstructured":"Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail MH. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98:1215\u201326. https:\/\/doi.org\/10.1093\/jnci\/djj332.","journal-title":"J Natl Cancer Inst"},{"key":"1062_CR20","doi-asserted-by":"publisher","first-page":"457","DOI":"10.7326\/M15-2934","volume":"165","author":"BL Sprague","year":"2016","unstructured":"Sprague BL, Conant EF, Onega T, Garcia MP, Beaber EF, Herschorn SD, Lehman CD, Tosteson AN, Lacson R, Schnall MD, Kontos D, Haas JS, Weaver DL, Barlow WE. Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study. Ann Intern Med. 2016;165:457\u201364. https:\/\/doi.org\/10.7326\/M15-2934.","journal-title":"Ann Intern Med"},{"key":"1062_CR21","volume-title":"ACR BI-RADS atlas, breast imaging reporting and data system","author":"C D\u2019Orsi","year":"2013","unstructured":"D\u2019Orsi C, Sickles E, Mendelson E, Morris E. ACR BI-RADS atlas, breast imaging reporting and data system. Reston, VA: American College of Radiology; 2013."},{"key":"1062_CR22","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1088\/0031-9155\/39\/10\/008","volume":"39","author":"JW Byng","year":"1994","unstructured":"Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39:1629\u201338. https:\/\/doi.org\/10.1088\/0031-9155\/39\/10\/008.","journal-title":"Phys Med Biol"},{"key":"1062_CR23","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1148\/radiol.2015151261","volume":"279","author":"KR Brandt","year":"2016","unstructured":"Brandt KR, Scott CG, Ma L, Mahmoudzadeh AP, Jensen MR, Whaley DH, Wu FF, Malkov S, Hruska CB, Norman AD, Heine J, Shepherd J, Pankratz VS, Kerlikowske K, Vachon CM. Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology. 2016;279:710\u20139. https:\/\/doi.org\/10.1148\/radiol.2015151261.","journal-title":"Radiology"},{"key":"1062_CR24","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1148\/radiol.2461070309","volume":"246","author":"DB Kopans","year":"2008","unstructured":"Kopans DB. Basic physics and doubts about relationship between Mammographically determined tissue density and breast Cancer risk. Radiology. 2008;246:348\u201353. https:\/\/doi.org\/10.1148\/radiol.2461070309.","journal-title":"Radiology"},{"key":"1062_CR25","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1093\/aje\/kws446","volume":"178","author":"M Lokate","year":"2013","unstructured":"Lokate M, Stellato RK, Veldhuis WB, Peeters PHM, Van Gils CH. Age-related changes in mammographic density and breast cancer risk. Am J Epidemiol. 2013;178:101\u20139. https:\/\/doi.org\/10.1093\/aje\/kws446.","journal-title":"Am J Epidemiol"},{"key":"1062_CR26","first-page":"151","volume":"9","author":"CM Vachon","year":"2000","unstructured":"Vachon CM, Kushi LH, Cerhan JR, Kuni CC, Sellers TA. Association of Diet and Mammographic Breast Density in the Minnesota breast Cancer family cohort Association of Diet and Mammographic Breast Density in the Minnesota breast Cancer family cohort. Cancer Epidemiol Biomark Prev. 2000;9:151\u201360.","journal-title":"Cancer Epidemiol Biomark Prev"},{"key":"1062_CR27","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"J Martin Bland","year":"1986","unstructured":"Martin Bland J, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307\u201310. https:\/\/doi.org\/10.1016\/S0140-6736(86)90837-8.","journal-title":"Lancet"},{"key":"1062_CR28","volume-title":"Breast imaging reporting and data system. In: American College of Radiology. Breast imaging reporting and data system (BI-RADS\u00ae )","author":"C D\u2019Orsi","year":"2003","unstructured":"D\u2019Orsi C, Sickles E, Mendelson E. Breast imaging reporting and data system. In: American College of Radiology. Breast imaging reporting and data system (BI-RADS\u00ae ). 4th ed ed. Reston, VA: American College of Radiology; 2003.","edition":"4"},{"key":"1062_CR29","doi-asserted-by":"publisher","first-page":"313","DOI":"10.3348\/kjr.2014.15.3.313","volume":"15","author":"SY Ko","year":"2014","unstructured":"Ko SY, Kim E-K, Kim MJ, Moon HJ. Mammographic density estimation with automated volumetric breast density measurement. Korean J Radiol. 2014;15:313\u201321. https:\/\/doi.org\/10.3348\/kjr.2014.15.3.313.","journal-title":"Korean J Radiol"},{"key":"1062_CR30","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1148\/radiol.2016152062","volume":"282","author":"AM Jeffers","year":"2017","unstructured":"Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast Cancer risk and mammographic density assessed with Semiautomated and fully automated methods and BI-RADS. Radiology. 2017;282:348\u201355. https:\/\/doi.org\/10.1148\/radiol.2016152062.","journal-title":"Radiology"},{"key":"1062_CR31","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1158\/1055-9965.EPI-09-0107","volume":"18","author":"N Boyd","year":"2009","unstructured":"Boyd N, Martin L, Gunasekara A, Melnichouk O, Maudsley G, Peressotti C, Yaffe M, Minkin S. Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes. Cancer Epidemiol Biomark Prev. 2009;18:1754\u201362. https:\/\/doi.org\/10.1158\/1055-9965.EPI-09-0107.","journal-title":"Cancer Epidemiol Biomark Prev"},{"key":"1062_CR32","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1111\/tbj.12443","volume":"21","author":"CC Gard","year":"2015","unstructured":"Gard CC, Aiello Bowles EJ, Miglioretti DL, Taplin SH, Rutter CM. Misclassification of breast imaging reporting and data system (BI-RADS) mammographic density and implications for breast density reporting legislation. Breast J. 2015;21:481\u20139. https:\/\/doi.org\/10.1111\/tbj.12443.","journal-title":"Breast J"},{"key":"1062_CR33","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1177\/0284185114554674","volume":"56","author":"HN Lee","year":"2015","unstructured":"Lee HN, Sohn YM, Han KH. Comparison of mammographic density estimation by Volpara software with radiologists\u2019 visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them. Acta Radiol. 2015;56:1061\u20138. https:\/\/doi.org\/10.1177\/0284185114554674.","journal-title":"Acta Radiol"},{"key":"1062_CR34","doi-asserted-by":"publisher","first-page":"692","DOI":"10.2214\/AJR.12.10197","volume":"201","author":"HM Gweon","year":"2013","unstructured":"Gweon HM, Youk JH, Kim JA, Son EJ. Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment. AJR Am J Roentgenol. 2013;201:692\u20137. https:\/\/doi.org\/10.2214\/AJR.12.10197.","journal-title":"AJR Am J Roentgenol"},{"key":"1062_CR35","doi-asserted-by":"publisher","first-page":"e85952","DOI":"10.1371\/journal.pone.0085952","volume":"9","author":"A Gubern-M\u00e9rida","year":"2014","unstructured":"Gubern-M\u00e9rida A, Kallenberg M, Platel B, Mann RM, Mart\u00ed R, Karssemeijer N. Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One. 2014;9:e85952. https:\/\/doi.org\/10.1371\/journal.pone.0085952.","journal-title":"PLoS One"},{"key":"1062_CR36","doi-asserted-by":"publisher","unstructured":"Highnam R, Sauber N, Destounis S. Breast density into clinical practice. Breast Imaging. 2012:466\u201373. https:\/\/doi.org\/10.1007\/978-3-642-31271-7_60.","DOI":"10.1007\/978-3-642-31271-7_60"},{"key":"1062_CR37","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.breast.2012.01.005","volume":"21","author":"S Ciatto","year":"2012","unstructured":"Ciatto S, Bernardi D, Calabrese M, Durando M, Gentilini MA, Mariscotti G, Monetti F, Moriconi E, Pesce B, Roselli A, Stevanin C, Tapparelli M, Houssami N. A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. Breast. 2012;21:503\u20136. https:\/\/doi.org\/10.1016\/j.breast.2012.01.005.","journal-title":"Breast"},{"key":"1062_CR38","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1148\/radiol.15141686","volume":"275","author":"O Alonzo-Proulx","year":"2015","unstructured":"Alonzo-Proulx O, Mawdsley GE, Patrie JT, Yaffe MJ, Harvey JA. Reliability of automated breast density measurements. Radiology. 2015;275:366\u201376. https:\/\/doi.org\/10.1148\/radiol.15141686.","journal-title":"Radiology"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-1062-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-020-1062-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-020-1062-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:20:03Z","timestamp":1614644403000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-020-1062-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,2]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1062"],"URL":"https:\/\/doi.org\/10.1186\/s12911-020-1062-y","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2020,3,2]]},"assertion":[{"value":"21 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was approved, and data were collected after a favorable opinion was issued (no 1,299,474) by the Research Ethics Committee (<i>CEP<\/i>) of the educational institution involved \u2013 the Federal University of Rio Grande do Norte (UFRN) - Brazil. Informed consent in writing was obtained from all participants.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Informed consent in writing was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"45"}}