{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:00:26Z","timestamp":1791302426025,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"GE Healthcare and Korean Society of Breast Imaging & Korean Society for Breast Screening","award":["KSBI&KSFBS-2020-03"],"award-info":[{"award-number":["KSBI&KSFBS-2020-03"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02306-2","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:32:20Z","timestamp":1791300740000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Factors Associated with False-Positive Marks and False-Negative Cancers on Automated Breast Ultrasound Using an Artificial Intelligence\u2013Based Computer-Aided Detection System"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-9711","authenticated-orcid":false,"given":"Haejung","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6519-7770","authenticated-orcid":false,"given":"Sun-Young","family":"Baek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-0571","authenticated-orcid":false,"given":"Boo-Kyung","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0399-7956","authenticated-orcid":false,"given":"Eun Sook","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1361-5269","authenticated-orcid":false,"given":"Ji Soo","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9074-8087","authenticated-orcid":false,"given":"Jeongmin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9228-022X","authenticated-orcid":false,"given":"Myoung Kyoung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6679-9650","authenticated-orcid":false,"given":"Eun Young","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"issue":"10016","key":"2306_CR1","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/S0140-6736(15)00774-6","volume":"387","author":"N Ohuchi","year":"2016","unstructured":"Ohuchi N, Suzuki A, Sobue T, et al. Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet. 2016;387(10016):341-348.","journal-title":"Lancet."},{"issue":"13","key":"2306_CR2","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1001\/jama.2012.388","volume":"307","author":"WA Berg","year":"2012","unstructured":"Berg WA, Zhang Z, Lehrer D, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012;307(13):1394-1404.","journal-title":"JAMA."},{"issue":"2","key":"2306_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1148\/radiol.2412051710","volume":"241","author":"WA Berg","year":"2006","unstructured":"Berg WA, Blume JD, Cormack JB, Mendelson EB. Operator dependence of physician-performed whole-breast US: lesion detection and characterization. Radiology. 2006;241(2):355-365.","journal-title":"Radiology."},{"issue":"3","key":"2306_CR4","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1148\/rg.2018170162","volume":"38","author":"JCM van Zelst","year":"2018","unstructured":"van Zelst JCM, Mann RM. Automated three-dimensional breast US for screening: technique, artifacts, and lesion characterization. Radiographics. 2018;38(3):663-683.","journal-title":"Radiographics."},{"issue":"10","key":"2306_CR5","doi-asserted-by":"publisher","DOI":"10.1148\/rg.230023","volume":"43","author":"AI Huppe","year":"2023","unstructured":"Huppe AI, Inciardi MF, Aripoli AM, Peterson JK, Smith CB, Winblad OD. Pearls and pitfalls of interpretation of automated breast US. Radiographics. 2023;43(10):e230023.","journal-title":"Radiographics."},{"issue":"2","key":"2306_CR6","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/s00330-017-5011-9","volume":"28","author":"A Vourtsis","year":"2018","unstructured":"Vourtsis A, Kachulis A. The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. Eur Radiol. 2018;28(2):592-601.","journal-title":"Eur Radiol."},{"issue":"7","key":"2306_CR7","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1016\/j.ejrad.2015.04.007","volume":"84","author":"YY An","year":"2015","unstructured":"An YY, Kim SH, Kang BJ. The image quality and lesion characterization of breast using automated whole-breast ultrasound: a comparison with handheld ultrasound. Eur J Radiol. 2015;84(7):1232-1235.","journal-title":"Eur J Radiol."},{"issue":"8","key":"2306_CR8","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1016\/j.ultrasmedbio.2019.04.028","volume":"45","author":"L Wang","year":"2019","unstructured":"Wang L, Qi ZH. Automatic breast volume scanner versus handheld ultrasound in differentiation of benign and malignant breast lesions: a systematic review and meta-analysis. Ultrasound Med Biol. 2019;45(8):1874-1881.","journal-title":"Ultrasound Med Biol."},{"issue":"3","key":"2306_CR9","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1148\/radiol.2019181816","volume":"292","author":"S Yang","year":"2019","unstructured":"Yang S, Gao X, Liu L, et al. Performance and reading time of automated breast US with or without computer-aided detection. Radiology. 2019;292(3):540-549.","journal-title":"Radiology."},{"issue":"3","key":"2306_CR10","doi-asserted-by":"publisher","first-page":"153","DOI":"10.5152\/ejbh.2019.4468","volume":"15","author":"A Arslan","year":"2019","unstructured":"Arslan A, Erta\u015f G, Ar\u0131bal E. 3D automated breast ultrasound system: comparison of interpretation time of senior versus junior radiologist. Eur J Breast Health. 2019;15(3):153-157.","journal-title":"Eur J Breast Health."},{"issue":"7","key":"2306_CR11","doi-asserted-by":"publisher","first-page":"2996","DOI":"10.1007\/s00330-017-5280-3","volume":"28","author":"JCM van Zelst","year":"2018","unstructured":"van Zelst JCM, Tan T, Clauser P, et al. Dedicated computer-aided detection software for automated 3D breast ultrasound: an efficient tool for the radiologist in supplemental screening of women with dense breasts. Eur Radiol. 2018;28(7):2996-3006.","journal-title":"Eur Radiol."},{"issue":"2","key":"2306_CR12","doi-asserted-by":"publisher","first-page":"452","DOI":"10.2214\/AJR.18.19516","volume":"211","author":"Y Jiang","year":"2018","unstructured":"Jiang Y, Inciardi MF, Edwards AV, Papaioannou J. Interpretation time using a concurrent-read computer-aided detection system for automated breast ultrasound in breast cancer screening of women with dense breast tissue. AJR Am J Roentgenol. 2018;211(2):452-461.","journal-title":"AJR Am J Roentgenol."},{"key":"2306_CR13","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejrad.2017.01.021","volume":"89","author":"JCM van Zelst","year":"2017","unstructured":"van Zelst JCM, Tan T, Platel B, et al. Improved cancer detection in automated breast ultrasound by radiologists using computer-aided detection. Eur J Radiol. 2017;89:54-59.","journal-title":"Eur J Radiol."},{"issue":"3","key":"2306_CR14","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1177\/0284185119858051","volume":"61","author":"JC van Zelst","year":"2020","unstructured":"van Zelst JC, Tan T, Mann RM, Karssemeijer N. Validation of radiologists\u2019 findings by computer-aided detection software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software. Acta Radiol. 2020;61(3):312-320.","journal-title":"Acta Radiol."},{"issue":"8","key":"2306_CR15","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1016\/j.ultrasmedbio.2018.04.020","volume":"44","author":"X Xu","year":"2018","unstructured":"Xu X, Bao L, Tan Y, Zhu L, Kong F, Wang W. 1000-case reader study of radiologists' performance in interpretation of automated breast volume scanner images with a computer-aided detection system. Ultrasound Med Biol. 2018;44(8):1694-1702.","journal-title":"Ultrasound Med Biol."},{"issue":"10","key":"2306_CR16","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.1007\/s00268-010-0683-1","volume":"34","author":"SPL Leong","year":"2010","unstructured":"Leong SPL, Shen ZZ, Liu TJ, et al. Is breast cancer the same disease in Asian and Western countries? World J Surg. 2010;34(10):2308-2324.","journal-title":"World J Surg."},{"issue":"2","key":"2306_CR17","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/s10549-016-4054-y","volume":"161","author":"N Rajaram","year":"2017","unstructured":"Rajaram N, Mariapun S, Eriksson M, et al. Differences in mammographic density between Asian and Caucasian populations: a comparative analysis. Breast Cancer Res Treat. 2017;161(2):353-362.","journal-title":"Breast Cancer Res Treat."},{"issue":"2","key":"2306_CR18","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.acra.2023.09.013","volume":"31","author":"MR Kwon","year":"2024","unstructured":"Kwon MR, Youn I, Lee MY, Lee HA. Diagnostic performance of artificial intelligence-based computer-aided detection software for automated breast ultrasound. Acad Radiol. 2024;31(2):480-491.","journal-title":"Acad Radiol."},{"key":"2306_CR19","doi-asserted-by":"publisher","unstructured":"Shin SU, Jang M, Yun B, et al. Effectiveness of AI-CAD software for breast cancer detection in automated breast ultrasound. J Imaging Inform Med. 2025. https:\/\/doi.org\/10.1007\/s10278-025-01786-y. Published online December 22, 2025.","DOI":"10.1007\/s10278-025-01786-y"},{"key":"2306_CR20","unstructured":"Mendelson EB, B\u00f6hm-V\u00e9lez M, Berg WA, Whitman GJ, Feldman MI, Madjar H, et al.: ACR BI-RADS\u00ae ultrasound. In: D\u2019Orsi CJ, Sickles EA, Mendelson EB, Morris EA, eds. ACR BI-RADS\u00ae atlas, breast imaging reporting and data system, 5th ed. Reston, VA: American College of Radiology, 2013."},{"key":"2306_CR21","doi-asserted-by":"publisher","first-page":"7771","DOI":"10.1007\/s00330-021-07864-3","volume":"31","author":"WJ Choi","year":"2021","unstructured":"Choi WJ, Kim SH, Shin HJ, et al. Automated breast US as the primary screening test for breast cancer among East Asian women aged 40-49 years: a multicenter prospective study. Eur Radiol. 2021;31:7771-7782.","journal-title":"Eur Radiol."},{"issue":"1","key":"2306_CR22","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1093\/jbi\/wbad077","volume":"6","author":"JS Choi","year":"2024","unstructured":"Choi JS, Tsunoda H, Moon WK. Nonmass lesions on breast US: an international perspective on clinical use and outcomes. J Breast Imaging. 2024;6(1):86-98.","journal-title":"J Breast Imaging."},{"issue":"3","key":"2306_CR23","doi-asserted-by":"crossref","first-page":"689","DOI":"10.3390\/diagnostics12030689","volume":"12","author":"J Lee","year":"2022","unstructured":"Lee J, Kang BJ, Kim SH, Park GE. Evaluation of computer-aided detection in screening automated breast ultrasound based on characteristics of CAD marks and false-positive marks. Diagnostics (Basel). 2022;12(3):583.","journal-title":"Diagnostics (Basel)."},{"issue":"2","key":"2306_CR24","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s10549-016-3758-3","volume":"156","author":"J Joekel","year":"2016","unstructured":"Joekel J, Eggemann H, Costa SD, Ignatov A. Should the hyperechogenic halo around malignant breast lesions be included in the measurement of tumor size? Breast Cancer Res Treat. 2016;156(2):311-317.","journal-title":"Breast Cancer Res Treat."},{"issue":"6","key":"2306_CR25","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1055\/s-0034-1385342","volume":"35","author":"T Durmus","year":"2014","unstructured":"Durmus T, St\u00f6ckel J, Slowinski T, Thomas A, Fischer T. The hyperechoic zone around breast lesions: an indirect parameter of malignancy. Ultraschall Med. 2014;35(6):547-553.","journal-title":"Ultraschall Med."},{"issue":"1","key":"2306_CR26","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s12282-013-0498-7","volume":"21","author":"T Uematsu","year":"2014","unstructured":"Uematsu T. Ultrasonographic findings of missed breast cancer: pitfalls and pearls. Breast Cancer. 2014;21(1):10-19.","journal-title":"Breast Cancer."},{"issue":"3","key":"2306_CR27","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1148\/rg.313105038","volume":"31","author":"MJ Kim","year":"2011","unstructured":"Kim MJ, Kim JY, Yoon JH, et al. How to find an isoechoic lesion with breast US. Radiographics. 2011;31(3):663-676.","journal-title":"Radiographics."},{"issue":"6","key":"2306_CR28","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1001\/jamainternmed.2014.981","volume":"174","author":"AN Tosteson","year":"2014","unstructured":"Tosteson AN, Fryback DG, Hammond CS, et al. Consequences of false-positive screening mammograms. JAMA Intern Med. 2014;174(6):954-961.","journal-title":"JAMA Intern Med."},{"issue":"8","key":"2306_CR29","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1016\/j.acra.2021.11.022","volume":"29","author":"N G\u00fcldogan","year":"2022","unstructured":"G\u00fcldogan N, Y\u0131lmaz E, Arslan A, K\u00fc\u00e7\u00fckkaya F, Atila N, Ar\u0131bal E. Comparison of 3D-automated breast ultrasound with handheld breast ultrasound regarding detection and BI-RADS characterization of lesions in dense breasts: a study of 592 cases. Acad Radiol. 2022;29(8):1143-1148.","journal-title":"Acad Radiol."},{"issue":"3","key":"2306_CR30","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222575","volume":"306","author":"RJ Acciavatti","year":"2023","unstructured":"Acciavatti RJ, Lee SH, Reig B, et al. Beyond breast density: risk measures for breast cancer in multiple imaging modalities. Radiology. 2023;306(3):e222575.","journal-title":"Radiology."},{"issue":"9","key":"2306_CR31","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1016\/j.jacr.2023.06.015","volume":"20","author":"S Tripathi","year":"2023","unstructured":"Tripathi S, Gabriel K, Dheer S, et al. Understanding biases and disparities in radiology AI datasets: a review. J Am Coll Radiol. 2023;20(9):836-841.","journal-title":"J Am Coll Radiol."},{"key":"2306_CR32","doi-asserted-by":"publisher","unstructured":"Choi EJ, Wang Y, Choi H, et al. Automated breast ultrasound features associated with diagnostic performance of a multiview convolutional neural network according to the level of experience of radiologists. Ultraschall Med. 2025. https:\/\/doi.org\/10.1055\/a-2643-9818. Published online August 19, 2025.","DOI":"10.1055\/a-2643-9818"},{"key":"2306_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103466","volume":"101","author":"X Tao","year":"2025","unstructured":"Tao X, Cao Y, Jiang Y, et al. Enhancing lesion detection in automated breast ultrasound using unsupervised multi-view contrastive learning with 3D DETR. Med Image Anal. 2025;101:103466.","journal-title":"Med Image Anal."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02306-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02306-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02306-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:32:21Z","timestamp":1791300741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02306-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,6]]},"references-count":33,"alternative-id":["2306"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02306-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,6]]},"assertion":[{"value":"20 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2023\u201306-131) and was conducted in accordance with the Declaration of Helsinki.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The requirement for written informed consent was waived by the Institutional Review Board due to the retrospective nature of the study.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}