{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T01:34:20Z","timestamp":1789608860269,"version":"build-2803163510"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Siemens Healthineers"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3\/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes\/no decision). The automated and manual diameters differed significantly (p &lt; 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p &lt; 0.05), similar in obese but significantly different in non-obese patients (p &lt; 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p &lt; 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r &gt; 0.84; ICC &gt; 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists\u2019 efficiency in clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics14090866","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T08:08:27Z","timestamp":1713859707000},"page":"866","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3089-9606","authenticated-orcid":false,"given":"Nicola","family":"Fink","sequence":"first","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basel","family":"Yacoub","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U. Joseph","family":"Schoepf","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emese","family":"Zsarnoczay","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Medical Imaging Center, Semmelweis University, Kor\u00e1nyi S\u00e1ndor utca 2, 1083 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1697-477X","authenticated-orcid":false,"given":"Daniel","family":"Pinos","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milan","family":"Vecsey-Nagy","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Heart and Vascular Center, Semmelweis University, Varosmajor utca 68, 1122 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6527-0974","authenticated-orcid":false,"given":"Saikiran","family":"Rapaka","sequence":"additional","affiliation":[{"name":"Siemens Healthineers, Princeton, NJ 08540, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puneet","family":"Sharma","sequence":"additional","affiliation":[{"name":"Siemens Healthineers, Princeton, NJ 08540, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2989-4485","authenticated-orcid":false,"given":"Jim","family":"O\u2019Doherty","sequence":"additional","affiliation":[{"name":"Siemens Medical Solutions, Malvern, PA 19355, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Ricke","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-7462","authenticated-orcid":false,"given":"Akos","family":"Varga-Szemes","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4156-7727","authenticated-orcid":false,"given":"Tilman","family":"Emrich","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA"},{"name":"Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Langenbeckstr. 1, 55131 Mainz, Germany"},{"name":"German Centre for Cardiovascular Research, 55131 Mainz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.jvs.2012.04.053","article-title":"Natural history of thoracic aortic aneurysms","volume":"56","author":"Kuzmik","year":"2012","journal-title":"J. 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