{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:56:06Z","timestamp":1792788966591,"version":"4.5.0"},"reference-count":26,"publisher":"IMR Press","issue":"9","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L232123"],"award-info":[{"award-number":["L232123"]}]},{"name":"Beijing Municipal Administration of Hospitals Incubating Program","award":["px2024024"],"award-info":[{"award-number":["px2024024"]}],"id":[{"id":"https:\/\/ror.org\/04baakq55","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Key R&D Program of China","award":["2022YFE0209800"],"award-info":[{"award-number":["2022YFE0209800"]}]},{"name":"Beijing Anzhen Hospital High Level Research","award":["2024AZC2002"],"award-info":[{"award-number":["2024AZC2002"]}],"id":[{"id":"https:\/\/ror.org\/02h2j1586","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Rev. Cardiovasc. Med."],"abstract":"<jats:p>Background: Accurate evaluation of coronary luminal stenosis using coronary computed tomography angiography (CCTA) remains significantly impaired in the presence of severe coronary artery calcification. Calcification-related blooming artifacts frequently lead to overestimation of stenosis severity and reduced diagnostic specificity, representing a persistent and unresolved challenge in clinical practice. Thus, this study aimed to develop and validate a self-supervised deep learning (DL) algorithm to improve the diagnostic performance of CCTA for assessing luminal stenosis in patients with heavily calcified coronary plaques (Agatston score &gt;300), using quantitative coronary angiography (QCA) as the reference standard. Methods: This multicenter retrospective study enrolled 786 patients with agatston score (AS) &gt;300 who underwent both CCTA and QCA from four Chinese hospitals. The dataset was randomly divided into a training group (80%, n = 628) for algorithm development and a validation group (20%, n = 158). The diagnostic performance of the DL model and radiologists was compared using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for detecting \u226550% diameter stenosis at the lesion, vessel, and patient levels. Reporting time was also evaluated. Results: In the validation cohort, 476 calcified lesions in 213 vessels from 93 patients were confirmed by QCA to have \u226550% diameter stenosis. The DL model demonstrated significantly higher specificity than radiologists at both the vessel level (52% vs. 30%) and patient level (74% vs. 57%) (all p &lt; 0.05). In lesions with severe calcification (cross-sectional calcium arc 270\u00b0\u2013360\u00b0), DL further improved lesion-level specificity and NPV by 27% and 36%, respectively (all p &lt; 0.05). In addition, DL markedly reduced reporting time compared with radiologists (61.9 \u00b1 2.56 s vs. 376.6 \u00b1 127.2 s; p &lt; 0.001). Conclusion: The proposed self-supervised DL model enables rapid and more accurate assessment of luminal stenosis in the presence of heavily calcified plaques, with improved diagnostic specificity and greater workflow efficiency.<\/jats:p>","DOI":"10.31083\/rcm49404","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:57:11Z","timestamp":1790693831000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Optimizes the Diagnostic Performance of Coronary Computed Tomography Angiography in Heavily Calcified Coronary Plaques: A Chinese Multicenter Study"],"prefix":"10.31083","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4295-8836","authenticated-orcid":true,"given":"Chunyan","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2544-3142","authenticated-orcid":true,"given":"Chengjin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Big Data and Statistics, Anhui University, 230601 Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4350-4199","authenticated-orcid":true,"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, 230093 Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5533-3350","authenticated-orcid":true,"given":"Benjamin","family":"Ellison","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-4439","authenticated-orcid":true,"given":"Zhifan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Sun Yatsen University, 518000 Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3820-0916","authenticated-orcid":true,"given":"Zhe","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Lixian Town Community Health Service Center, 101400 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1093-1346","authenticated-orcid":true,"given":"Bin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7383-7571","authenticated-orcid":true,"given":"Jiayin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated First People\u2019s Hospital, 200080 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1490-242X","authenticated-orcid":true,"given":"Ximing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 200080 Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3258-2813","authenticated-orcid":true,"given":"Longjiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Jinling Hospital, The Medical School of Nanjing University, 210002 Nanjing, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1095-6420","authenticated-orcid":true,"given":"Heye","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Sun Yatsen University, 518000 Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-0448","authenticated-orcid":true,"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-2178","authenticated-orcid":true,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, 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