{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:37:06Z","timestamp":1790768226779,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Local Research and Development Council in \u00d6sterg\u00f6tland","award":["03007103"],"award-info":[{"award-number":["03007103"]}]},{"name":"Grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement","award":["R\u00d6-991454"],"award-info":[{"award-number":["R\u00d6-991454"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Objectives<\/jats:title>\n            <jats:p>Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>In total, 5057 CSCT examinations were performed on one CT system at Link\u00f6ping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland\u2013Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50\u201364 years of age (mean 57.3\u2009\u00b1\u20094.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001), respectively. Bland\u2013Altman analyses rendered mean differences \u00b1\u20091.96\u2009SD upper and lower limits of agreement for AS \u22120.04, \u221252.5 to 52.4, VS \u22120.44, \u221246.51 to 45.63, and MS \u22120.07, \u22129.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Question<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>Can AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Findings<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>This study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Clinical relevance<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>This study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.<\/jats:italic>\n            <\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00330-024-11118-3","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T01:02:08Z","timestamp":1729213328000},"page":"2438-2447","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5485-6769","authenticated-orcid":false,"given":"Lilian","family":"Henriksson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e5rten","family":"Sandstedt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrik","family":"Nowik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anders","family":"Persson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"11118_CR1","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1016\/j.ejrad.2016.09.028","volume":"85","author":"AM den Harder","year":"2016","unstructured":"den Harder AM, Wolterink JM, Willemink MJ, Schilham AM, de Jong PA (2016) Submillisievert coronary calcium quantification using model-based iterative reconstruction: a within-patient analysis. 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This author did not have control over the data at any point during the study. The rest of the authors have no potential conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"We consulted statistician Lars Walter, Research and Development Unit in Local Health Care, Link\u00f6ping University, Link\u00f6ping, Sweden. He is not an author.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"To this date, a total of 45 articles have reported on parts or the entire included study population. None of these articles have addressed AI evaluation of CSCT scans.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}