{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T10:42:18Z","timestamp":1793356938307,"version":"4.5.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The University of Osaka","id":[{"id":"https:\/\/ror.org\/035t8zc32","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate how two fixed computer-aided detection (CAD) operating modes\u2014sensitivity-optimized (SE-CAD) and specificity-optimized (SP-CAD)\u2014affect radiologists\u2019 diagnostic performance in chest radiography (CXR). Previous studies have focused on the performance of standalone CAD at different threshold settings. However, in clinical practice, CAD is commonly used as a decision-support tool for radiologists. This study aimed to evaluate the impact of different CAD threshold settings on radiologists\u2019 diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective single-center diagnostic accuracy study evaluated posteroanterior or anteroposterior CXRs acquired between 2013 and 2023, interpreted with and without AI assistance, using CT as the reference standard. Six radiologists (three residents \/ three board-certified) read all cases four times over two sessions: first without CAD, then with either SE_CAD or SP_CAD, and after 1-month washout period, second session using the alternative CAD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      375 patients (mean age, 65\u2009\u00b1\u200915 years; 170 women) were included. The dataset comprised 139 cases with pulmonary opacities (nodules \/ masses \/ consolidations), 119 with pleural effusion, 29 with pneumothorax, and 161 normal cases, including overlaps. SP_CAD-assisted readings showed higher specificity than SE_CAD-assisted for pulmonary opacities (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Sensitivity did not differ significantly between SE_CAD and SP_CAD assistance. For pulmonary opacities, CAD-assisted reading showed higher sensitivity (both CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) but lower specificity (SE_CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006; SP_CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.03) than unassisted reading. For pleural effusion, CAD-assisted improved sensitivity (SE_CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; SP_CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.02) and specificity (both CAD:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) than unassisted reading. For pneumothorax, SE_CAD assistance did not significantly affect sensitivity but decreased specificity, whereas SP_CAD assistance improved sensitivity without significantly affecting specificity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The specificity-prioritized CAD showed higher specificity than the sensitivity-prioritized CAD, without a significant reduction in sensitivity. These findings suggest that different CAD operating points can influence radiologists\u2019 diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Secondary abstract<\/jats:title>\n                    <jats:p>We compared radiologist performance using sensitivity-optimized and specificity-optimized computer-aided detection (CAD). Different predefined CAD operating points influenced radiologist performance, resulting in differences in specificity for nodules\/masses\/consolidations. Our results show that different preset decision thresholds within the same AI model can influence diagnostic performance in AI-assisted radiograph interpretation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-026-02053-y","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T06:46:40Z","timestamp":1785739600000},"page":"1970-1981","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic performance with sensitivity\/specificity optimized computer-aided detection to detect common abnormalities in chest radiography"],"prefix":"10.1007","volume":"44","author":[{"given":"Miyuki","family":"Tomiyama","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0911-6769","authenticated-orcid":false,"given":"Masahiro","family":"Yanagawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsubasa","family":"Yoshida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuniaki","family":"Ohara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiaki","family":"Masuda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keisuke","family":"Ninomiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhei","family":"Doi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Nakamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Hori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Hashimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Tomiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"issue":"1","key":"2053_CR1","doi-asserted-by":"publisher","first-page":"25151","DOI":"10.1038\/s41598-024-76608-2","volume":"14","author":"PG Anderson","year":"2024","unstructured":"Anderson PG, Tarder-Stoll H, Alpaslan M, Keathley N, Levin DL, Venkatesh S, et al. Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays. Sci Rep. 2024;14(1):25151.","journal-title":"Sci Rep"},{"issue":"5","key":"2053_CR2","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.1007\/s00330-021-08397-5","volume":"32","author":"KN Jin","year":"2022","unstructured":"Jin KN, Kim EY, Kim YJ, Lee GP, Kim H, Oh S, et al. Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study. Eur Radiol. 2022;32(5):3469\u201379.","journal-title":"Eur Radiol"},{"issue":"8","key":"2053_CR3","doi-asserted-by":"publisher","first-page":"e2229289","DOI":"10.1001\/jamanetworkopen.2022.29289","volume":"5","author":"JS Ahn","year":"2022","unstructured":"Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, et al. Association of Artificial Intelligence\u2013Aided Chest Radiograph Interpretation With Reader Performance and Efficiency. JAMA Netw Open. 2022;5(8):e2229289.","journal-title":"JAMA Netw Open"},{"key":"2053_CR4","doi-asserted-by":"publisher","first-page":"111783","DOI":"10.1016\/j.ejrad.2024.111783","volume":"181","author":"S Sridharan","year":"2024","unstructured":"Sridharan S, Seah Xin Hui A, Venkataraman N, Sivanath Tirukonda P, Pratab Jeyaratnam R, John S, et al. Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study. Eur J Radiol. 2024;181:111783.","journal-title":"Eur J Radiol"},{"issue":"11","key":"2053_CR5","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1055\/a-2271-0799","volume":"196","author":"CA Stueckle","year":"2024","unstructured":"Stueckle CA, Haage P. The radiologist as a physician \u2013 artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians \u2013 a narrative review. Rofo. 2024;196(11):1115\u201324.","journal-title":"Rofo"},{"issue":"11","key":"2053_CR6","doi-asserted-by":"publisher","first-page":"7255","DOI":"10.1007\/s00330-024-10794-5","volume":"34","author":"S Schalekamp","year":"2024","unstructured":"Schalekamp S, Van Leeuwen K, Calli E, Murphy K, Rutten M, Geurts B, et al. Performance of AI to exclude normal chest radiographs to reduce radiologists\u2019 workload. Eur Radiol. 2024;34(11):7255\u201363.","journal-title":"Eur Radiol"},{"issue":"1","key":"2053_CR7","doi-asserted-by":"publisher","first-page":"5492","DOI":"10.1038\/s41598-020-62148-y","volume":"10","author":"K Murphy","year":"2020","unstructured":"Murphy K, Habib SS, Zaidi SMA, Khowaja S, Khan AJ, Melendez J, et al. Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep. 2020;10(1):5492. https:\/\/doi.org\/10.1038\/s41598-020-62148-y.","journal-title":"Sci Rep"},{"issue":"10","key":"2053_CR8","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.3348\/kjr.2022.0189","volume":"23","author":"H Yoo","year":"2022","unstructured":"Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, et al. Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort. Korean J Radiol. 2022;23(10):1009.","journal-title":"Korean J Radiol"},{"issue":"2","key":"2053_CR9","doi-asserted-by":"publisher","first-page":"157","DOI":"10.5588\/ijtld.22.0437","volume":"27","author":"J Fehr","year":"2023","unstructured":"Fehr J, Gunda R, Siedner MJ, Hanekom W, Ndung\u00b4u T, Grant A, et al. CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities. int j tuberc lung dis. 2023;27(2):157\u201360.","journal-title":"int j tuberc lung dis"},{"issue":"3","key":"2053_CR10","doi-asserted-by":"publisher","first-page":"e517","DOI":"10.1016\/S2214-109X(24)00516-3","volume":"13","author":"AJ Scott","year":"2025","unstructured":"Scott AJ, Perumal T, Pooran A, Oelofse S, Jaumdally S, Swanepoel J, et al. Clinical evaluation of computer-aided digital x-ray detection of pulmonary tuberculosis during community-based screening or active case-finding: a case\u2013control study. Lancet Global Health. 2025;13(3):e517\u201327.","journal-title":"Lancet Global Health"},{"key":"2053_CR11","doi-asserted-by":"publisher","first-page":"e240272","DOI":"10.1148\/radiol.240272","volume":"312","author":"LL Plesner","year":"2024","unstructured":"Plesner LL, M\u00fcller FC, Brejneb\u00f8l MW, Krag CH, Laustrup LC, Rasmussen F, et al. Using AI to identify unremarkable chest radiographs for automatic reporting. Radiology. 2024;312:e240272.","journal-title":"Radiology"},{"key":"2053_CR12","doi-asserted-by":"crossref","unstructured":"Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015; 351:h5527.","DOI":"10.1136\/bmj.h5527"},{"key":"2053_CR13","doi-asserted-by":"crossref","unstructured":"Ghesu FC, Georgescu B, Gibson E, Guendel S, Kalra MK, Singh R et al. Quantifying and leveraging classification uncertainty for chest radiograph assessment. IMICCAI. 2019; 676\u201384.","DOI":"10.1007\/978-3-030-32226-7_75"},{"key":"2053_CR14","doi-asserted-by":"crossref","unstructured":"\u00c7all\u0131 E, Sogancioglu E, van Ginneken B, Murphy K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 2021 72:102125.","DOI":"10.1016\/j.media.2021.102125"},{"issue":"15","key":"2053_CR15","doi-asserted-by":"publisher","first-page":"2586","DOI":"10.1080\/03610920802610084","volume":"38","author":"BD Gallas","year":"2009","unstructured":"Gallas BD, Bandos A, Samuelson FW, Wagner RF. A Framework for Random-Effects ROC Analysis: Biases with the Bootstrap and Other Variance Estimators. Commun Stat - Theory Methods. 2009;38(15):2586\u2013603.","journal-title":"Commun Stat - Theory Methods"},{"issue":"3","key":"2053_CR16","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/bmt.2012.244","volume":"48","author":"Y Kanda","year":"2013","unstructured":"Kanda Y. Investigation of the freely available easy-to-use software \u2018EZR\u2019 for medical statistics. Bone Marrow Transpl. 2013;48(3):452\u20138.","journal-title":"Bone Marrow Transpl"},{"key":"2053_CR17","doi-asserted-by":"crossref","unstructured":"Kim EY, Kim YJ, Choi WJ, Lee GP, Choi YR, Jin KN et al. Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort. Alongi P, editor. PLoS ONE. 2021;16(2):e0246472.","DOI":"10.1371\/journal.pone.0246472"},{"key":"2053_CR18","doi-asserted-by":"crossref","unstructured":"Shin HJ, Han K, Ryu L, Kim EK. The impact of artificial intelligence on the reading times of radiologists for chest radiographs. npj Digit Med. 2023;6(1).","DOI":"10.1038\/s41746-023-00829-4"},{"issue":"12","key":"2053_CR19","doi-asserted-by":"publisher","first-page":"e2141096","DOI":"10.1001\/jamanetworkopen.2021.41096","volume":"4","author":"F Homayounieh","year":"2021","unstructured":"Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, et al. An Artificial Intelligence\u2013Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study. JAMA Netw Open. 2021;4(12):e2141096.","journal-title":"JAMA Netw Open"},{"key":"2053_CR20","doi-asserted-by":"crossref","unstructured":"Bennani S, Regnard N-E, Ventre J, Lassalle L, Nguyen T, Ducarouge A et al. Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiol 2023 309:e230860.","DOI":"10.1148\/radiol.230860"},{"key":"2053_CR21","doi-asserted-by":"publisher","first-page":"8263","DOI":"10.1007\/s00330-023-09747-1","volume":"33","author":"MH Bernstein","year":"2023","unstructured":"Bernstein MH, Atalay MK, Dibble EH, Aaron WPM, Adib RK, Saurabh A, et al. Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography. Eur Radiol. 2023;33:8263\u20139.","journal-title":"Eur Radiol"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-026-02053-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-026-02053-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-026-02053-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T10:15:27Z","timestamp":1793355327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-026-02053-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"references-count":21,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["2053"],"URL":"https:\/\/doi.org\/10.1007\/s11604-026-02053-y","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"16 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Financial interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"M. Yanagawa and N. Tomiyama have received research support and payment for lectures from Canon Medical Systems Corporation and FUJIFILM Medical Corporation. M. Hori has received research support and payment for lectures from FUJIFILM Corporation. The other authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective multicenter study was approved by the institutional review board of each hospital (The University of Osaka Hospital: approval number, 23254).","order":3,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was waived for review of the patients\u2019 records and images.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective multicenter study was approved by the institutional review board of each hospital, and informed consent was waived for this retrospective study.","order":5,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective multicenter study was approved by the institutional review board of each hospital, and informed consent was waived for this retrospective study.","order":6,"name":"Ethics","label":"Consent to publish","group":{"name":"EthicsHeading","label":"Declarations"}}]}}