Peer-Reviewed Publication
J Digit Imaging2022;35(4):962-969.August 1, 2022Journal Article
Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs.
Noam Barda1,2, Noa Dagan3,4, Amos Stemmer5, Janni Yuval3,6, Eitan Bachmat7, Eldad Elnekave8, Ran Balicer3,9
1Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel. noamba@clalit.org.il.
2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. noamba@clalit.org.il.
3Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel.
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
5Institute of Oncology, Sheba Medical Center, Ramat Gan, Israel.
6Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel.
7Department of Computer Science, Ben Gurion University of the Negev, Beer-Sheva, Israel.
8Zebra Medical Vision Ltd, Hutzot, Shefayim, Israel. eldad.elnekave@gmail.com.
9School of Public Health, Ben Gurion University of the Negev, Beer-Sheva, Israel.
Abstract
Cardiovascular disease (CVD) prediction models are widely used in modern medicine and are incorporated into prominent guidelines. Coronary artery calcium (CAC) is a marker of coronary atherosclerotic disease and has proven utility for predicting cardiovascular disease. Despite this, current guidelines recommend against including CAC scores in CVD prediction models due to the medical and financial…
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