{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:59:42Z","timestamp":1790171982606,"version":"4.1.0"},"reference-count":18,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>To compare the accuracy of coronary artery calcium scores (CACSs) automatically measured by two artificial intelligence (AI) systems on nonelectrocardiogram (ECG)-gated chest computed tomography (CT) and to investigate the relationship between CACS and the degree of coronary artery stenosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>A retrospective study was conducted on 182 patients who underwent both ECG-gated Coronary computed tomography angiography (CTA) and non-ECG-gated chest CT. Images from all patients were processed using postprocessing software and two AI systems (Software A\/B). The manually measured CACS by experienced physicians using ECG-gated Coronary CTA was used as the gold standard. The agreement between two AI systems and the gold standard was assessed using the intraclass correlation coefficient (ICC) and Bland\u2013Altman analysis. In addition, Spearman\u2019s correlation analysis was performed to evaluate the relationship between CACS and the degree of coronary artery stenosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Both AI systems demonstrated high ICCs for CACS measurement (0.90, 95% confidence interval [CI]: 0.871\u20130.926 for Software A; 0.96, 95% CI: 0.943\u20130.968 for Software B). The mean differences (95% limits of agreement) were 23.59 \u00b1 442.35 for Software A and \u221280.35 \u00b1 292.99 for Software B. Both systems showed good agreement in CACS measurement, with Software B exhibiting higher accuracy than Software A. Spearman\u2019s correlation analysis revealed weak-to-moderate positive correlations between CACS measured by different methods and the degree of coronary artery stenosis (\n                      <jats:italic toggle=\"yes\">r<\/jats:italic>\n                      = 0.151\u20130.560,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05) across all coronary branches.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The two AI-based CACS systems, when applied to nongated chest CT, demonstrated consistency with manually measured CACS from ECG-gated coronary CTA. Software B showed superior accuracy compared to Software A. The correlation between CACS and the degree of vascular stenosis is influenced by vascular remodeling patterns, with positive remodeling reducing its consistency. Therefore, a comprehensive assessment of vascular stenosis should integrate vascular remodeling patterns and other diagnostic indicators.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jrms.jrms_1200_25","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:02:08Z","timestamp":1790168528000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of different artificial intelligence-based coronary artery calcium scoring algorithms in nongated chest computed tomography"],"prefix":"10.4103","volume":"31","author":[{"given":"Ting","family":"Li","sequence":"first","affiliation":[{"name":"Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong Province, China"},{"name":"Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodie","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Shandong Second Medical University, Weifang, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqing","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohai","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiji","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,6]]},"reference":[{"key":"R1-20260923","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1001\/jamacardio.2016.5493","article-title":"Association of coronary artery calcium in adults aged 32 to 46 years with incident coronary heart disease and death","volume":"2","author":"Carr","year":"2017","journal-title":"JAMA 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Imaging"},{"key":"R14-20260923","doi-asserted-by":"crossref","first-page":"e001832","DOI":"10.1136\/openhrt-2021-001832","article-title":"Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence","volume":"8","author":"Jonas","year":"2021","journal-title":"Open Heart"},{"key":"R15-20260923","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1161\/01.CIR.101.6.598","article-title":"Extent and direction of arterial remodeling in stable versus unstable coronary syndromes: An intravascular ultrasound study","volume":"101","author":"Schoenhagen","year":"2000","journal-title":"Circulation"},{"key":"R16-20260923","first-page":"937","article-title":"AI opportunistic coronary calcium screening at veterans affairs hospitals","volume":"2","author":"Hagopian","year":"2025","journal-title":"NEJM AI"},{"key":"R17-20260923","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1001\/jamainternmed.2022.1262","article-title":"Evaluation of the incremental 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