{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T09:18:51Z","timestamp":1778750331091,"version":"3.51.4"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004047","name":"Karolinska Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004047","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Coronary Computed Tomography Angiography (CCTA) is an established tool for assessing coronary artery disease. CCTA determined total coronary artery plaque volume predicts cardiovascular events, but manual quantification is impractical for routine use. Deep learning-based methods offer a promising solution for automated plaque volume assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed and validated a novel software,\n                      <jats:italic>QuantiPlaque<\/jats:italic>\n                      , powered by deep learning models, for automated segmentation of total and calcified coronary plaque volume. Expert manual annotation served as the reference standard. Correlations between deep learning and expert segmentation were evaluated using intraclass correlation coefficient (ICC), Pearson\u2019s r, and Spearman\u2019s rho, and agreements were evaluated with Bland-Altman analyses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 115 CCTA scans were included. Mean (range) age was 58 (51\u201364) and 51% were females. The mean coronary artery calcium score was 56 Agatston units, ranging from 0 to 601. The model demonstrated strong correlation and agreement with expert annotation for per-patient total plaque volume (ICC 0.95, mean difference\u2009\u2212\u20098.35 mm\n                      <jats:sup>3<\/jats:sup>\n                      , 95% limit of agreement\u2009\u2212\u2009102 to 85 mm\n                      <jats:sup>3<\/jats:sup>\n                      , Spearman\u2019s rho 0.87, Pearson\u2019s r 0.95) and calcified plaque volume (ICC 0.90, mean difference\u2009\u2212\u20090.28 mm\n                      <jats:sup>3<\/jats:sup>\n                      , 95% limits of agreement\u2009\u2212\u200921.97 to 21.42 mm\n                      <jats:sup>3<\/jats:sup>\n                      , Spearman\u2019s rho 0.93, Pearson\u2019s r 0.90). Per-vessel analysis showed strong correlation in the left anterior descending artery and right coronary artery but was weaker in the left circumflex artery territory.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The evaluated deep learning model provides accurate quantification of total and calcified plaque burden, with strong correlation and agreement to expert annotation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-026-02379-z","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T08:25:43Z","timestamp":1778747143000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a novel deep learning coronary artery plaque quantification model"],"prefix":"10.1186","volume":"26","author":[{"given":"Johan","family":"Malmqvist","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas","family":"Jernberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramtin","family":"Vedad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunliang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"issue":"3","key":"2379_CR1","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.jcct.2020.11.001","volume":"15","author":"J Narula","year":"2021","unstructured":"Narula J, Chandrashekhar Y, Ahmadi A, Abbara S, Berman DS, Blankstein R, et al. SCCT 2021 Expert Consensus Document on Coronary Computed Tomographic Angiography: A Report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr. 2021;15(3):192\u2013217.","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"4","key":"2379_CR2","doi-asserted-by":"publisher","first-page":"433","DOI":"10.7326\/M22-3027","volume":"176","author":"A Fuchs","year":"2023","unstructured":"Fuchs A, K\u00fchl JT, Sigvardsen PE, Afzal S, Knudsen AD, M\u00f8ller MB, et al. Subclinical Coronary Atherosclerosis and Risk for Myocardial Infarction in a Danish Cohort: A Prospective Observational Cohort Study. Ann Intern Med. 2023;176(4):433\u201342.","journal-title":"Ann Intern Med"},{"issue":"24","key":"2379_CR3","doi-asserted-by":"publisher","first-page":"2803","DOI":"10.1016\/j.jacc.2020.10.021","volume":"76","author":"MB Mortensen","year":"2020","unstructured":"Mortensen MB, Dzaye O, Steffensen FH, B\u00f8tker HE, Jensen JM, R\u00f8nnow Sand NP, et al. Impact of Plaque Burden Versus Stenosis on Ischemic Events in Patients With Coronary Atherosclerosis. J Am Coll Cardiol. 2020;76(24):2803\u201313.","journal-title":"J Am Coll Cardiol"},{"issue":"1","key":"2379_CR4","doi-asserted-by":"publisher","first-page":"e000096","DOI":"10.1136\/openhrt-2014-000096","volume":"1","author":"F Plank","year":"2014","unstructured":"Plank F, Friedrich G, Dichtl W, Klauser A, Jaschke W, Franz WM, Feuchtner G. The diagnostic and prognostic value of coronary CT angiography in asymptomatic high-risk patients: a cohort study. Open Heart. 2014;1(1):e000096.","journal-title":"Open Heart"},{"issue":"4","key":"2379_CR5","doi-asserted-by":"publisher","first-page":"e256","DOI":"10.1016\/S2589-7500(22)00022-X","volume":"4","author":"A Lin","year":"2022","unstructured":"Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256\u201365.","journal-title":"Lancet Digit Health"},{"key":"2379_CR6","doi-asserted-by":"crossref","unstructured":"Dahdal J, Jukema RA, Maaniitty T, Nurmohamed NS, Raijmakers PG, Hoek R et al. CCTA-derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients. Eur Heart J Cardiovasc Imaging. 2025.","DOI":"10.1093\/ehjci\/jeaf093"},{"key":"2379_CR7","unstructured":"Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al. Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI). JACC Cardiovasc Imaging. 2022;15(11):1974\u20132001. CAD-RADS\u2122 2.0\u20132022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular."},{"key":"2379_CR8","doi-asserted-by":"crossref","unstructured":"Dong YL, Bai X, Tian M, Zhang C, Zhuang X, Jernberg T. C. Wang. coDice: connectivity-preserving dice loss for2D\/3D tubular structure segmentation. SPIE Medical Imaging; 2025.","DOI":"10.1117\/12.3047030"},{"key":"2379_CR9","unstructured":"Wang C. RM, \u00d6. Smedby. Vessel segmentation using implicit model-guided level sets. 3D cardiovascular imaging: a MICCAI segmentation challenge workshop. 2012."},{"issue":"8","key":"2379_CR10","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/j.media.2013.05.007","volume":"17","author":"HA Kirisli","year":"2013","unstructured":"Kirisli HA, Schaap M, Metz CT, Dharampal AS, Meijboom WB, Papadopoulou SL, et al. 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Eur Radiol. 2025;35(8):4461\u201371.","journal-title":"Eur Radiol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-026-02379-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02379-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02379-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T08:51:22Z","timestamp":1778748682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-026-02379-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":13,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2379"],"URL":"https:\/\/doi.org\/10.1186\/s12880-026-02379-z","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]},"assertion":[{"value":"27 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2026","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 study has been approved by the Swedish Ethical Review Authority (Dnr 2023-02237-01) and written informed consent has been obtained from all study participants. All methods were carried out in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"253"}}