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      <comment>Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States            
            Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States            
            Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States            
            Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen, Tübingen, Germany            
            Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan            
            Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary            
            School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany            
            Department of Mathematics and Statistics, Boston University, Boston, MA, United States            
            Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States            
            National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study, Framingham, MA, United States            
            Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States            
            Department of Medicine, Division of Cardiology, Duke University School of Medicine, Duke Clinical Research Institute, Durham, NC, United States            
            Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States            
            Radiology and Nuclear Medicine, CARIM &amp; GROW, Maastricht University, Maastricht, Netherlands            
            Export Date: 30 June 2025; Cited By: 170; Correspondence Address: H.J.W.L. Aerts; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, United States; email: haerts@bwh.harvard.edu</comment>
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          <label>48. Kannel, W. B., Feinleib, M., McNamara, P. M., Garrison, R. J. &amp; Castelli, W. P. An investigation of coronary heart disease in families. The Framingham offspring study. Am. J. Epidemiol. 110, 281–290 (1979)., DOI: 10.1093/oxfordjournals.aje.a112813</label>
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          <label>49. Splansky, G. L. et al. The third generation cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: design, recruitment, and initial examination. Am. J. Epidemiol. 165, 1328–1335 (2007)., DOI: 10.1093/aje/kwm021</label>
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          <label>50. Ronneberger, O., Fischer, P. &amp; Brox, T. U-Net: convolutional networks for biomedical image segmentation. Lecture Notes Computer Sci. 9351, 234–241 (2015)., DOI: 10.1007/978-3-319-24574-4_28</label>
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          <label>53. Ahrens, H. &amp; Searle, S. R. Linear Models. John Wiley &amp; Sons, Inc., New York-London-Sydney-Toronto 1971. XXI, 532 S. $9.50. Biometrische Z. 16, 78–79 (1974)., DOI: 10.1002/bimj.19740160113</label>
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      <label>Zeleznik Roman et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. (2021) NATURE COMMUNICATIONS 2041-1723 12 1</label><template>&lt;div class=&quot;JournalArticle Publication short-list&quot;&gt; &lt;div class=&quot;authors&quot;&gt; &lt;span class=&quot;author-name&quot; &gt; Zeleznik, Roman &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Foldyna, Borek &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Eslami, Parastou &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Weiss, Jakob &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Alexander, Ivanov &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Taron, Jana &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Parmar, Chintan &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Alvi, Raza M. &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Banerji, Dahlia &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Uno, Mio &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; et al. &lt;/div &gt;&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;31926670&quot; mtid=&quot;31926670&quot; target=&quot;_blank&quot;&gt;Deep convolutional neural networks to predict cardiovascular risk from computed tomography&lt;/a&gt;&lt;/div&gt; &lt;div class=&quot;pub-info&quot;&gt; &lt;span class=&quot;journal-title&quot;&gt;NATURE COMMUNICATIONS&lt;/span&gt; &lt;span class=&quot;journal-volume&quot;&gt;12&lt;/span&gt; : &lt;span class=&quot;journal-issue&quot;&gt;1&lt;/span&gt; &lt;span class=&quot;page&quot;&gt; Paper: 715 , 9 p. &lt;/span&gt; &lt;span class=&quot;year&quot;&gt;(2021)&lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;pub-end&quot;&gt;&lt;div class=&quot;identifier-list&quot;&gt; &lt;span class=&quot;identifiers&quot;&gt; &lt;span class=&quot;id identifier oa_GOLD&quot; title=&quot; Gold &quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;10.1038/s41467-021-20966-2&quot; target=&quot;_blank&quot; href=&quot;https://doi.org/10.1038/s41467-021-20966-2&quot;&gt; DOI &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;000684846200014&quot; target=&quot;_blank&quot; href=&quot;https://www.webofscience.com/wos/woscc/full-record/000684846200014&quot;&gt; WoS &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;85100121682&quot; target=&quot;_blank&quot; href=&quot;http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-85100121682&quot;&gt; Scopus &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;33514711&quot; target=&quot;_blank&quot; href=&quot;http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;list_uids=33514711&amp;dopt=Abstract&quot;&gt; PubMed &lt;/a&gt; &lt;/span&gt; &lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;short-pub-prop-list&quot;&gt; &lt;span class=&quot;short-pub-mtid&quot;&gt; Közlemény:31926670 &lt;/span&gt; &lt;span class=&quot;status-holder&quot;&gt;&lt;span class=&quot;status-data status-VALIDATED&quot;&gt; Egyeztetett &lt;/span&gt;&lt;/span&gt; &lt;span class=&quot;pub-core&quot;&gt;Forrás Idéző &lt;/span&gt; &lt;span class=&quot;pub-type&quot;&gt;Folyóiratcikk (Szakcikk ) &lt;/span&gt; &lt;!-- &amp;&amp; !record.category.scientific --&gt; &lt;span class=&quot;pub-category&quot;&gt;Tudományos&lt;/span&gt; &lt;div class=&quot;publication-citation&quot; style=&quot;margin-left: 0.5cm;&quot;&gt; &lt;span title=&quot;Nyilvános idézőközlemények összesen, említések nélkül&quot; class=&quot;citingPub-count&quot;&gt;Nyilvános idéző összesen: 207&lt;/span&gt; | Független: 194 | Függő: 13 | Nem jelölt: 0 | WoS jelölt: 182 | Scopus jelölt:&amp;nbsp;191 | WoS/Scopus jelölt:&amp;nbsp;207 | DOI jelölt:&amp;nbsp;199 (Nem nyilvános:&amp;nbsp;2) &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;</template><template2>&lt;div class=&quot;JournalArticle Publication long-list&quot;&gt;
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		&lt;div class=&quot;autype autype0&quot;&gt;				&lt;span class=&quot;author-name&quot; &gt;Zeleznik Roman
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							&lt;span class=&quot;author-name&quot; &gt;Uno Mio
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							&lt;span class=&quot;author-name&quot; mtid=&quot;10051170&quot;&gt;&lt;a 
																				   href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10051170&quot; target=&quot;_blank&quot;&gt;Karády Júlia
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            &lt;span class=&quot;authorAux-mtmt&quot;&gt; orvostudományok, kardiológia&lt;/span&gt;)
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&lt;span class=&quot;author-affil&quot;&gt;&lt;span title=&quot;Semmelweis Egyetem&quot;&gt;SE&lt;/span&gt;/&lt;span title=&quot;Általános Orvostudományi Kar&quot;&gt;AOK&lt;/span&gt;/&lt;span title=&quot;Klinikum&quot;&gt;K&lt;/span&gt;/Városmajori Szív- és Érgyógyászati Klinika&lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Zhang Lili
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							&lt;span class=&quot;author-name&quot; &gt;Scholtz Jan-Erik
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							&lt;span class=&quot;author-name&quot; &gt;Mayrhofer Thomas
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							&lt;span class=&quot;author-name&quot; &gt;Vasan Ramachandran S.
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							&lt;span class=&quot;author-name&quot; &gt;Douglas Pamela S.
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							&lt;span class=&quot;author-name&quot; &gt;Hoffmann Udo
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							&lt;span class=&quot;author-name&quot; &gt;Lu Michael T.
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							&lt;span class=&quot;author-name&quot; &gt;Aerts Hugo J. W. L. ✉
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&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;31926670&quot; target=&quot;_blank&quot;&gt;Deep convolutional neural networks to predict cardiovascular risk from computed tomography&lt;/a&gt;&lt;/div&gt;    &lt;div&gt;		&lt;span class=&quot;journal-title&quot;&gt;NATURE COMMUNICATIONS&lt;/span&gt;

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&lt;div class=&quot;funder&quot;&gt; (HA: 866504) Támogató: EU-ERC   &lt;/div&gt;
&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2022.02.03. 11:41 Szalóky-Siki Ágnes (SE_KK_Admin5_SZSA, admin)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States            
            Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States            
            Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Me...&lt;/pre&gt;
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