{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:42:48Z","timestamp":1793295768780,"version":"4.5.0"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thebreastonline.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Breast"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.breast.2022.07.009","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T13:09:44Z","timestamp":1658149784000},"page":"124-135","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":35,"special_numbering":"C","title":["Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation"],"prefix":"10.1016","volume":"65","author":[{"given":"Yi-Chen","family":"Lai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jen-Feng","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Jun","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting-Ting","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Jen","family":"Chiou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib1","first-page":"7","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA A Cancer J Clin"},{"issue":"S1","key":"10.1016\/j.breast.2022.07.009_bib2","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1002\/cncr.11022","article-title":"Mammography screening in African American women","volume":"97","author":"Jones","year":"2003","journal-title":"Cancer"},{"key":"10.1016\/j.breast.2022.07.009_bib3","series-title":"Diagnosis and staging of breast cancer: when and how to use mammography, tomosynthesis, ultrasound, contrast-enhanced mammography, and magnetic resonance imaging","first-page":"155","author":"Gilbert","year":"2019"},{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib4","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1148\/radiol.2021203476","article-title":"Breast cancer mortality rates have stopped declining in U.S. Women younger than 40 years","volume":"299","author":"Hendrick","year":"2021","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib5","doi-asserted-by":"crossref","DOI":"10.2214\/AJR.10.6049","article-title":"The relationship of mammographic density and age: implications for breast cancer screening","volume":"198","author":"Checka","year":"2012","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib6","doi-asserted-by":"crossref","DOI":"10.1186\/s12880-021-00565-9","article-title":"The relationship between breast density, age, and mammographic lesion type among Chinese breast cancer patients from a large clinical dataset","volume":"21","author":"Ji","year":"2021","journal-title":"BMC Med Imag"},{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib7","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics8010020","article-title":"The role of ultrasound in screening dense breasts\u2014a review of the literature and practical solutions for implementation","volume":"8","author":"Thigpen","year":"2018","journal-title":"Diagnostics"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib8","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1148\/radiol.2019181816","article-title":"Performance and reading time of automated breast us with or without computer-aided detection","volume":"292","author":"Yang","year":"2019","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.breast.2022.07.009_bib9","doi-asserted-by":"crossref","first-page":"452","DOI":"10.2214\/AJR.18.19516","article-title":"Interpretation time using a concurrent-read computer-aided detection system for automated breast ultrasound in breast cancer screening of women with dense breast tissue","volume":"211","author":"Jiang","year":"2018","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/jum.15684","article-title":"Diagnostic performance of an artificial intelligence system in breast ultrasound","volume":"41","author":"O'Connell","year":"2022","journal-title":"J Ultrasound Med"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib11","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/s10278-018-0132-5","article-title":"Impact of data presentation on physician performance utilizing artificial intelligence-based computer-aided diagnosis and decision support systems","volume":"32","author":"Barinov","year":"2019","journal-title":"J Digit Imag"},{"issue":"6","key":"10.1016\/j.breast.2022.07.009_bib12","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.2214\/AJR.19.21872","article-title":"Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment","volume":"214","author":"Mango","year":"2020","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib13","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.diii.2017.01.001","article-title":"BI-RADS\u00ae fifth edition: a summary of changes","volume":"98","author":"Spak","year":"2017","journal-title":"Diagnostic and Interventional Imaging"},{"key":"10.1016\/j.breast.2022.07.009_bib14","series-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)","first-page":"282","article-title":"Conditional convolutions for instance segmentation","volume":"vol. 12346","author":"Tian","year":"2020"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib15","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1148\/radiol.2018181371","article-title":"Detection of breast cancer with mammography: effect of an artificial intelligence support system","volume":"290","author":"Rodr\u00edguez-Ruiz","year":"2019","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib16","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"10.1016\/j.breast.2022.07.009_bib17","first-page":"18","article-title":"Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities","volume":"11316","author":"Smith","year":"2020","journal-title":"Proc SPIE-Int Soc Opt Eng"},{"issue":"2","key":"10.1016\/j.breast.2022.07.009_bib18","first-page":"285","volume":"24","author":"Obuchowski","year":"2007","journal-title":"Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests an anova approach with dependent observations"},{"issue":"4","key":"10.1016\/j.breast.2022.07.009_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.5.4.045503","article-title":"Multireader sample size program for diagnostic studies: demonstration and methodology","volume":"5","author":"Hillis","year":"2018","journal-title":"J Med Imaging"},{"key":"10.1016\/j.breast.2022.07.009_bib20","author":"Liang"},{"issue":"3","key":"10.1016\/j.breast.2022.07.009_bib21","doi-asserted-by":"crossref","first-page":"825","DOI":"10.2307\/2532642","article-title":"Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses","volume":"47","author":"Prentice","year":"1991","journal-title":"Biometrics"},{"issue":"2","key":"10.1016\/j.breast.2022.07.009_bib23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v015.i02","article-title":"The R package geepack for generalized estimating equations","volume":"15","author":"Halekoh","year":"2006","journal-title":"J Stat Software"},{"issue":"6","key":"10.1016\/j.breast.2022.07.009_bib24","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1002\/sim.1650","article-title":"Estimating equations for association structures","volume":"23","author":"Yan","year":"2004","journal-title":"Stat Med"},{"key":"10.1016\/j.breast.2022.07.009_bib25","first-page":"12","volume":"2\/3","author":"Yan","year":"2002"},{"key":"10.1016\/j.breast.2022.07.009_bib26","series-title":"Introductory biostatistics","author":"Le","year":"2016"},{"issue":"1","key":"10.1016\/j.breast.2022.07.009_bib27","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1148\/radiol.12120218","article-title":"Computer-aided detection of masses at mammography: interactive decision support versus prompts","volume":"266","author":"Hupse","year":"2013","journal-title":"Radiology"},{"key":"10.1016\/j.breast.2022.07.009_bib28","series-title":"Paired t test","author":"Hsu","year":"2014"},{"issue":"19","key":"10.1016\/j.breast.2022.07.009_bib29","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13194805","article-title":"Mammographic density changes over time and breast cancer risk: a systematic review and meta-analysis","volume":"13","author":"Mokhtary","year":"2021","journal-title":"Cancers"},{"issue":"6","key":"10.1016\/j.breast.2022.07.009_bib30","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002335","article-title":"Mammographic density and ageing: a collaborative pooled analysis of cross-sectional data from 22 countries worldwide","volume":"14","author":"Burton","year":"2017","journal-title":"PLoS Med"},{"issue":"10","key":"10.1016\/j.breast.2022.07.009_bib31","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1158\/1055-9965.EPI-20-0358","article-title":"Breast cancer population attributable risk proportions associated with body mass index and breast density by race\/ethnicity and menopausal status","volume":"29","author":"Bissell","year":"2020","journal-title":"Cancer Epidemiol Biomark Prev"},{"key":"10.1016\/j.breast.2022.07.009_bib32","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.mri.2019.08.026","article-title":"Evaluation of TP53\/PIK3CA mutations using texture and morphology analysis on breast MRI","volume":"63","author":"Moon","year":"2019","journal-title":"Magn Reson Imaging"},{"issue":"6","key":"10.1016\/j.breast.2022.07.009_bib33","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.mri.2016.03.001","article-title":"Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI","volume":"34","author":"Chang","year":"2016","journal-title":"Magn Reson Imaging"}],"container-title":["The Breast"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0960977622001266?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0960977622001266?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T08:45:56Z","timestamp":1760777156000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0960977622001266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":32,"alternative-id":["S0960977622001266"],"URL":"https:\/\/doi.org\/10.1016\/j.breast.2022.07.009","relation":{},"ISSN":["0960-9776"],"issn-type":[{"value":"0960-9776","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation","name":"articletitle","label":"Article Title"},{"value":"The Breast","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.breast.2022.07.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}]}}