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        <reference>
          <otype>Reference</otype>
          <mtid>19836352</mtid>
          <link>/api/reference/19836352</link>
          <label>1. Fonseca, C.G., Backhaus, M., Bluemke, D.A., The Cardiac Atlas Project–an imaging database for computational modeling and statistical atlases of the heart (2011) Bioinformatics, 27 (16), pp. 2288-2295</label>
          <listPosition>1</listPosition>
          <published>false</published>
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        <reference>
          <otype>Reference</otype>
          <mtid>19836351</mtid>
          <link>/api/reference/19836351</link>
          <label>2. Kwan, A., McElhinney, P., Tamarappoo, B., Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score (2020) Eur Radiol, 31 (3), pp. 1227-1235. , In press</label>
          <listPosition>2</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836350</mtid>
          <link>/api/reference/19836350</link>
          <label>3. Dey, D., Gaur, S., Ovrehus, K.A., Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study (2018) Eur Radiol, 28 (6), pp. 2655-2664</label>
          <listPosition>3</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836349</mtid>
          <link>/api/reference/19836349</link>
          <label>4. Motwani, M., Dey, D., Berman, D.S., Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis (2017) Eur Heart J, 38 (7), pp. 500-507</label>
          <listPosition>4</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836348</mtid>
          <link>/api/reference/19836348</link>
          <label>5. Tamarappoo, B.K., Lin, A., Commandeur, F., Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: a prospective study (2020) Atherosclerosis, 318, pp. 76-82</label>
          <listPosition>5</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836347</mtid>
          <link>/api/reference/19836347</link>
          <label>6. Eisenberg, E., McElhinney Priscilla, A., Commandeur, F., Deep learning–based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects (2020) Circulation: Cardiovascular Imaging, 13 (2)</label>
          <listPosition>6</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836346</mtid>
          <link>/api/reference/19836346</link>
          <label>7. Nakanishi, R., Slomka, P., Rios, R., Mahine learning adds to clincial and CAC assessment in predicting 1-year CHD and CVD deaths (2020) JACC Cardiovasc Imaging, 14 (3), pp. 615-625. , In press</label>
          <listPosition>7</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836345</mtid>
          <link>/api/reference/19836345</link>
          <label>8. Wynants, L., van Smeden, M., McLernon, D.J., Three myths about risk thresholds for prediction models (2019) BMC Med, 17 (1), p. 192</label>
          <listPosition>8</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836344</mtid>
          <link>/api/reference/19836344</link>
          <label>9. Tesche, C., Otani, K., De Cecco, C.N., Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry (2020) JACC Cardiovasc Imaging, 13 (3), pp. 760-770</label>
          <listPosition>9</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836343</mtid>
          <link>/api/reference/19836343</link>
          <label>10. Coenen, A., Kim, Y.H., Kruk, M., Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE Consortium (2018) Circ Cardiovasc Imaging, 11 (6)</label>
          <listPosition>10</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836342</mtid>
          <link>/api/reference/19836342</link>
          <label>11. Itu, L., Rapaka, S., Passerini, T., A machine-learning approach for computation of fractional flow reserve from coronary computed tomography (1985) J Appl Physiol, 121 (1), pp. 42-52. , 2016</label>
          <listPosition>11</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836341</mtid>
          <link>/api/reference/19836341</link>
          <label>12. Tesche, C., De Cecco, C.N., Albrecht, M.H., Coronary CT angiography–derived fractional flow reserve (2017) Radiology, 285 (1), pp. 17-33</label>
          <listPosition>12</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836340</mtid>
          <link>/api/reference/19836340</link>
          <label>13. Driessen Roel, S., Danad, I., Stuijfzand Wijnand, J., Comparison of coronary computed tomography angiography, fractional flow reserve, and perfusion imaging for ischemia diagnosis (2019) J Am Coll Cardiol, 73 (2), pp. 161-173</label>
          <listPosition>13</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836339</mtid>
          <link>/api/reference/19836339</link>
          <label>14. Hong, Y., Commandeur, F., Cadet, S., Deep learning-based stenosis quantification from coronary CT Angiography (2019) Proc SPIE-Int Soc Opt Eng, 10949, p. 109492I</label>
          <listPosition>14</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836338</mtid>
          <link>/api/reference/19836338</link>
          <label>15. Kang, D., Dey, D., Slomka, P.J., Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography (2015) J Med Imaging, 2 (1)</label>
          <listPosition>15</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836337</mtid>
          <link>/api/reference/19836337</link>
          <label>16. Arbab-Zadeh, A., Hoe, J., Quantification of coronary arterial stenoses by multidetector CT angiography in comparison with conventional angiography methods, caveats, and implications (2011) JACC Cardiovasc Imaging, 4 (2), pp. 191-202</label>
          <listPosition>16</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836336</mtid>
          <link>/api/reference/19836336</link>
          <label>17. Commandeur, F., Goeller, M., Razipour, A., Fully automated CT quantification of epicardial adipose tissue by deep learning: a multicenter study (2019) Radiology: Artif Intell, 1 (6)</label>
          <listPosition>17</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836335</mtid>
          <link>/api/reference/19836335</link>
          <label>18. Goeller, M., Achenbach, S., Marwan, M., Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects (2018) J Cardiovasc Comput Tomogr, 12 (1), pp. 67-73</label>
          <listPosition>18</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836334</mtid>
          <link>/api/reference/19836334</link>
          <label>19. Dey, D., Wong, N.D., Tamarappoo, B., Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome (2010) Atherosclerosis, 209 (1), pp. 136-141</label>
          <listPosition>19</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836333</mtid>
          <link>/api/reference/19836333</link>
          <label>20. Mahabadi, A.A., Berg, M.H., Lehmann, N., Association of epicardial fat with cardiovascular risk factors and incident myocardial infarction in the general population: the Heinz Nixdorf Recall Study (2013) J Am Coll Cardiol, 61 (13), pp. 1388-1395</label>
          <listPosition>20</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836332</mtid>
          <link>/api/reference/19836332</link>
          <label>21. Lin, A., Dey, D., Wong, D.T.L., Nerlekar, N., Perivascular adipose tissue and coronary atherosclerosis: from biology to imaging phenotyping (2019) Curr Atherosclerosis Rep, 21 (12), p. 47</label>
          <listPosition>21</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836331</mtid>
          <link>/api/reference/19836331</link>
          <label>22. van Velzen, S.G.M., Lessmann, N., Velthuis, B.K., Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols (2020) Radiology, 295 (1), pp. 66-79</label>
          <listPosition>22</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836330</mtid>
          <link>/api/reference/19836330</link>
          <label>23. Wolterink, J.M., Leiner, T., Takx, R.A., Viergever, M.A., Isgum, I., Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection (2015) IEEE Trans Med Imag, 34 (9), pp. 1867-1878</label>
          <listPosition>23</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836329</mtid>
          <link>/api/reference/19836329</link>
          <label>24. Wolterink, J., Leiner, T., Takx, R., Viergever, M., Isgum, I., An automatic machine learning system for coronary calcium scoring in clinical non-contrast enhanced, ECG-triggered cardiac CT. Vol vol. 9035. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 90352014</label>
          <listPosition>24</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836328</mtid>
          <link>/api/reference/19836328</link>
          <label>25. Budoff, M.J., Young, R., Burke, G., Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA) (2018) Eur Heart J, 39 (25), pp. 2401-2408</label>
          <listPosition>25</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836327</mtid>
          <link>/api/reference/19836327</link>
          <label>26. Wolterink, J., Leiner, T., Išgum, I., Graph convolutional networks for coronary artery segmentation in cardiac CT angiography (2019) International Workshop on Graph Learning in Medical Imaging, pp. 62-69. , Springer</label>
          <listPosition>26</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836326</mtid>
          <link>/api/reference/19836326</link>
          <label>27. Wolterink, J.M., van Hamersvelt, R.W., Viergever, M.A., Leiner, T., Isgum, I., Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier (2019) Med Image Anal, 51, pp. 46-60</label>
          <listPosition>27</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836325</mtid>
          <link>/api/reference/19836325</link>
          <label>28. Gülsün, M., Funka-Lea, G., Sharma, P., Rapaka, S., Zheng, Y., Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning (2016)</label>
          <listPosition>28</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836324</mtid>
          <link>/api/reference/19836324</link>
          <label>29. Kelm, B.M., Mittal, S., Zheng, Y., Detection, grading and classification of coronary stenoses in computed tomography angiography (2011) Med Image Comput Comput Assist Interv, 14, pp. 25-32</label>
          <listPosition>29</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836323</mtid>
          <link>/api/reference/19836323</link>
          <label>30. Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G., A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes (2009) Med Image Anal, 13 (6), pp. 819-845</label>
          <listPosition>30</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836322</mtid>
          <link>/api/reference/19836322</link>
          <label>31. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D., Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features (2008) IEEE Trans Med Imag, 27 (11), pp. 1668-1681</label>
          <listPosition>31</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836321</mtid>
          <link>/api/reference/19836321</link>
          <label>32. Al, W.A., Jung, H.Y., Yun, I.D., Jang, Y., Park, H.-B., Chang, H.-J., Automatic aortic valve landmark localization in coronary CT angiography using colonial walk (2018) PloS One, 13 (7). , e0200317</label>
          <listPosition>32</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836320</mtid>
          <link>/api/reference/19836320</link>
          <label>33. Liang, L., Kong, F., Martin, C., Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images (2017) Int J Numer Method Biomed Eng, 33 (5)</label>
          <listPosition>33</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836319</mtid>
          <link>/api/reference/19836319</link>
          <label>34. Grbic, S., Ionasec, R., Vitanovski, D., Complete valvular heart apparatus model from 4D cardiac CT (2012) Med Image Anal, 16 (5), pp. 1003-1014</label>
          <listPosition>34</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836318</mtid>
          <link>/api/reference/19836318</link>
          <label>35. Klein, R., Ametepe, E.S., Yam, Y., Dwivedi, G., Chow, B.J., Cardiac CT assessment of left ventricular mass in mid-diastasis and its prognostic value (2017) Eur Heart J Cardiovasc Imaging, 18 (1), pp. 95-102</label>
          <listPosition>35</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836317</mtid>
          <link>/api/reference/19836317</link>
          <label>36. Baskaran, L., Maliakal, G., Al'Aref, S.J., Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise (2020) Deep-Learning Method. JACC: Cardiovascular Imaging, 13 (5), pp. 1163-1171</label>
          <listPosition>36</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836316</mtid>
          <link>/api/reference/19836316</link>
          <label>37. CT myocardium segmentation (2020) Med Phys, 47 (4), pp. 1775-1785</label>
          <listPosition>37</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836315</mtid>
          <link>/api/reference/19836315</link>
          <label>38. Jun Guo, B., He, X., Lei, Y., Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography</label>
          <listPosition>38</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836314</mtid>
          <link>/api/reference/19836314</link>
          <label>39. Zreik, M., Leiner, T., De Vos, B., van Hamersvelt, R., Viergever, M., Isgum, I., Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks (2016) IEEE.Int. Symp. Biomed. Imag., pp. 40-43</label>
          <listPosition>39</listPosition>
          <published>false</published>
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        <reference>
          <otype>Reference</otype>
          <mtid>19836313</mtid>
          <link>/api/reference/19836313</link>
          <label>40. Bruns, S., Wolterink, J.M., Takx, R.A.P., Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT (2020) Med Phys, 47 (10), pp. 5048-5060</label>
          <listPosition>40</listPosition>
          <published>false</published>
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        <reference>
          <otype>Reference</otype>
          <mtid>19836312</mtid>
          <link>/api/reference/19836312</link>
          <label>41. Greupner, J., Zimmermann, E., Grohmann, A., Head-to-Head comparison of left ventricular function assessment with 64-row computed tomography, biplane left cineventriculography, and both 2- and 3-dimensional transthoracic echocardiography (2012) J Am Coll Cardiol, 59 (21), p. 1897</label>
          <listPosition>41</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836311</mtid>
          <link>/api/reference/19836311</link>
          <label>42. Murphy, D.J., Lavelle, L.P., Gibney, B., O'Donohoe, R.L., Rémy-Jardin, M., Dodd, J.D., Diagnostic accuracy of standard axial 64-slice chest CT compared to cardiac MRI for the detection of cardiomyopathies (2016) Br J Radiol, 89 (1059), p. 20150810. , 20150810</label>
          <listPosition>42</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836310</mtid>
          <link>/api/reference/19836310</link>
          <label>43. Jung, S., Lee, S., Jeon, B., Jang, Y., Chang, H., Deep learning cross-phase style transfer for motion artifact correction in coronary computed tomography angiography (2020) IEEE Access, 8, pp. 81849-81863</label>
          <listPosition>43</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836309</mtid>
          <link>/api/reference/19836309</link>
          <label>44. Lossau, T., Nickisch, H., Wissel, T., Motion estimation and correction in cardiac CT angiography images using convolutional neural networks (2019) Comput Med Imag Graph, 76, p. 101640</label>
          <listPosition>44</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836308</mtid>
          <link>/api/reference/19836308</link>
          <label>45. Kang, E., Koo, H.J., Yang, D.H., Seo, J.B., Ye, J.C., Cycle-consistent adversarial denoising network for multiphase coronary CT angiography (2019) Med Phys, 46 (2), pp. 550-562</label>
          <listPosition>45</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836307</mtid>
          <link>/api/reference/19836307</link>
          <label>46. Green, M., Marom, E.M., Konen, E., Kiryati, N., Mayer, A., 3-D neural denoising for low-dose coronary CT angiography (CCTA) (2018) Comput Med Imag Graph, 70, pp. 185-191</label>
          <listPosition>46</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836306</mtid>
          <link>/api/reference/19836306</link>
          <label>47. Kang, E., Min, J., Ye, J.C., A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction (2017) Med Phys, 44 (10), pp. e360-e375</label>
          <listPosition>47</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836305</mtid>
          <link>/api/reference/19836305</link>
          <label>48. Chen, H., Zhang, Y., Zhang, W., Low-dose CT via convolutional neural network (2017) Biomed Opt Express, 8 (2), pp. 679-694</label>
          <listPosition>48</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836304</mtid>
          <link>/api/reference/19836304</link>
          <label>49. Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I., Generative adversarial networks for noise reduction in low-dose CT (2017) IEEE Trans Med Imag, 36 (12), pp. 2536-2545</label>
          <listPosition>49</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836303</mtid>
          <link>/api/reference/19836303</link>
          <label>50. Bradley, A.P., The use of the area under the ROC curve in the evaluation of machine learning algorithms (1997) Pattern Recogn, 30 (7), pp. 1145-1159</label>
          <listPosition>50</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836302</mtid>
          <link>/api/reference/19836302</link>
          <label>51. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M., Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations (2017) Lecture Notes in Computer Science, pp. 240-248. , M. Cardoso et al. (eds.) Springer</label>
          <listPosition>51</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836301</mtid>
          <link>/api/reference/19836301</link>
          <label>52. Dey, D., Slomka, P.J., Leeson, P., Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review (2019) J Am Coll Cardiol, 73 (11), pp. 1317-1335</label>
          <listPosition>52</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836300</mtid>
          <link>/api/reference/19836300</link>
          <label>53. Turing, A.M., Computing machiner and intelligence (1950) Mind, 59 (236), pp. 433-460</label>
          <listPosition>53</listPosition>
          <published>false</published>
          <snippet>true</snippet>
        </reference>
        <reference>
          <otype>Reference</otype>
          <mtid>19836299</mtid>
          <link>/api/reference/19836299</link>
          <label>54. Lin, A., Kolossváry, M., Išgum, I., Maurovich-Horvat, P., Slomka, P.J., Dey, D., Artificial intelligence: improving the efficiency of cardiovascular imaging (2020) Expet Rev Med Dev, 17 (6), pp. 565-577</label>
          <listPosition>54</listPosition>
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      <label>Lin A. et al. Artificial intelligence in cardiovascular CT: Current status and future implications. (2021) JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY 1934-5925 1876-861X 15 6 462-469</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; Lin, A. &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10050627&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10050627&quot; target=&quot;_blank&quot;&gt;Kolossváry, M.&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Motwani, M. &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Išgum, I. &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; mtid=&quot;10019530&quot;&gt; &lt;a href=&quot;/gui2/?type=authors&amp;mode=browse&amp;sel=10019530&quot; target=&quot;_blank&quot;&gt;Maurovich-Horvat, P.&lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Slomka, P.J. &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Dey, D. ✉ &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; &lt;/div &gt;&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;31966262&quot; mtid=&quot;31966262&quot; target=&quot;_blank&quot;&gt;Artificial intelligence in cardiovascular CT: Current status and future implications&lt;/a&gt;&lt;/div&gt; &lt;div class=&quot;pub-info&quot;&gt; &lt;span class=&quot;journal-title&quot;&gt;JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY&lt;/span&gt; &lt;span class=&quot;journal-volume&quot;&gt;15&lt;/span&gt; : &lt;span class=&quot;journal-issue&quot;&gt;6&lt;/span&gt; &lt;span class=&quot;page&quot;&gt; pp. 462-469. , 8 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_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;10.1016/j.jcct.2021.03.006&quot; target=&quot;_blank&quot; href=&quot;https://doi.org/10.1016/j.jcct.2021.03.006&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;000714977600004&quot; target=&quot;_blank&quot; href=&quot;https://www.webofscience.com/wos/woscc/full-record/000714977600004&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;85103596707&quot; target=&quot;_blank&quot; href=&quot;http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-85103596707&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;33812855&quot; target=&quot;_blank&quot; href=&quot;http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;list_uids=33812855&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:31966262 &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 (Összefoglaló cikk ) &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: 47&lt;/span&gt; | Független: 36 | Függő: 11 | Nem jelölt: 0 | WoS jelölt: 41 | Scopus jelölt:&amp;nbsp;41 | WoS/Scopus jelölt:&amp;nbsp;47 | DOI jelölt:&amp;nbsp;47 (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;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;31966262&quot; target=&quot;_blank&quot;&gt;Artificial intelligence in cardiovascular CT: Current status and future implications&lt;/a&gt;&lt;/div&gt;    &lt;div&gt;		&lt;span class=&quot;journal-title&quot;&gt;JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY&lt;/span&gt;

        &lt;span class=&quot;journal-issn&quot;&gt;(&lt;a target=&quot;_blank&quot; href=&quot;https://portal.issn.org/resource/ISSN/1934-5925&quot;&gt;1934-5925&lt;/a&gt; &lt;a target=&quot;_blank&quot; href=&quot;https://portal.issn.org/resource/ISSN/1876-861X&quot;&gt;1876-861X&lt;/a&gt;)&lt;/span&gt;:
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			Angol
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Forrás	 Idéző
	
	
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2022.09.17. 14:55 Kolossváry Márton József (Kardiológia)
&lt;/div&gt;




	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: Funding Agency and Grant Number: National Heart, Lung, and Blood Institute [1R01HL133616, 1R01HL148787-01A1]
            Funding text: This work was supported in part by grants from the National Heart, Lung, and Blood Institute [1R01HL133616 and 1R01HL148787-01A1] .&lt;/pre&gt;
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