Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols
Open accessAbstract
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199–568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1–10, 11–100, 101–400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79–0.97 for CAC and 0.66–0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84–0.99 (CAC) and 0.92–0.99 (TAC) for CT protocol–specific training and to 0.85–0.99 (CAC) and 0.96–0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol–specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.
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A combined solution capable of analyzing cardiac and non-gated chest CT as presented in our investigation has only been proposed by de Vos et al. and van Velzen et al.
“A combined solution capable of analyzing cardiac and non-gated chest CT as presented in our investigation has only been proposed by de Vos et al.36 and van Velzen et al.41.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [41]
The title in our indexed copy identifies van Velzen et al. (Radiology, 2020, pp. 66–79) as a study of automatic calcium scoring across cardiac CT and chest CT protocols. The available passages do not specify that the chest CT was non-gated or establish the sentence’s “only” claim; searches for non-gated CT and a combined method found no further substantive passage. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOther studies that investigated deep learning algorithms for automated coronary calcium quantification used smaller cohorts or proprietary technologies.
“Although other studies have investigated deep learning algorithms for automated coronary calcium quantification34–42, they used smaller cohorts or proprietary technologies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [41]
The paper’s title and Abstract identify it as a study of deep-learning-based automatic calcium scoring in CT, supporting its inclusion among studies investigating automated coronary calcium quantification. The available passages do not establish that it used a smaller cohort or proprietary technology; searches for those comparisons found no matching passage. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Cites
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064 · see citing works
Quantification of coronary artery calcium using ultrafast computed tomography
Agatston, A. S., Janowitz, W. R., Hildner, F. J., Zusmer, N. R., Viamonte, M., & Detrano, R. (1990) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(90)90282-t · Cited by 7,786 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Grundy, S. M., Stone, N. J., Bailey, A. L., Beam, C., Birtcher, K. K., Blumenthal, R. S., Braun, L. T., de Ferranti, S., Faiella-Tommasino, J., Forman, D. E., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000625 · Cited by 6,185 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works
Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B. H. J., et al. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.015 · Cited by 1,408 · see citing works
Long-Term Prognosis Associated With Coronary Calcification
Budoff, M. J., Shaw, L. J., Liu, S. T., Weinstein, S. R., Tseng, P. H., Flores, F. R., Callister, T. Q., Raggi, P., Berman, D. S., & Mosler, T. P. (2007) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2006.10.079 · Cited by 1,341 · see citing works
Grundy, S. M., Stone, N. J., Bailey, A. L., Beam, C. T., Birtcher, K. K., Blumenthal, R. S., Braun, L. T., de Ferranti, S. D., Faiella-Tommasino, J., Forman, D. E., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000624 · Cited by 920 · see citing works
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
Taylor, H. A., WILSON, J. G., Jones, D. W., Sarpong, D. F., Srinivasan, A., Garrison, R. J., Nelson, C. R., & Wyatt, S. B. (2005) · PubMed · Cited by 525 · see citing works
Hecht, H. S., Cronin, P., Blaha, M. J., Budoff, M. J., Kazerooni, E. A., Narula, J., Yankelevitz, D., & Abbara, S. (2017) · Journal of Cardiovascular Computed Tomography · doi:10.1016/j.jcct.2016.11.003 · Cited by 492 · see citing works
Blaha, M. J., Cainzos-Achirica, M., Greenland, P., McEvoy, J. W., Blankstein, R., Budoff, M. J., Dardari, Z., Sibley, C. T., Burke, G. L., Kronmal, R. A., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.018524 · Cited by 485 · see citing works
Carr, J. J., Jacobs, D. R., Terry, J. G., Shay, C. M., Sidney, S., Liu, K., Schreiner, P. J., Lewis, C. E., Shikany, J. M., Reis, J. P., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.5493 · Cited by 361 · see citing works
Self-citationChiles, C., Duan, F., Gladish, G. W., Ravenel, J. G., Baginski, S. G., Snyder, B. S., DeMello, S., Desjardins, S. S., Munden, R. F., & For the NLST Study Team (2015) · Radiology · doi:10.1148/radiol.15142062 · Cited by 356 · see citing works
Shemesh, J., Henschke, C. I., Shaham, D., Yip, R., Farooqi, A. O., Cham, M. D., McCauley, D. I., Chen, M., Smith, J. P., Libby, D. M., et al. (2010) · Radiology · doi:10.1148/radiol.10100383 · Cited by 319 · see citing works
Coronary Artery Calcium Scanning
Hecht, H. S. (2015) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2015.02.006 · Cited by 306 · see citing works
Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions
Lessmann, N., van Ginneken, B., Zreik, M., de Jong, P. A., de Vos, B. D., Viergever, M. A., & Isgum, I. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2769839 · Cited by 263 · see citing works
Self-citationJacobs, P. C. A., Gondrie, M. J. A., van der Graaf, Y., de Koning, H. J., Išgum, I., van Ginneken, B., & Mali, W. P. T. M. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.10.5577 · Cited by 178 · see citing works
Self-citationConvNet-Based Localization of Anatomical Structures in 3-D Medical Images
de Vos, B. D., Wolterink, J. M., de Jong, P. A., Leiner, T., Viergever, M. A., & Isgum, I. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2673121 · Cited by 133 · see citing works
Self-citationAutomated Coronary Artery Calcification Scoring in Non-Gated Chest CT: Agreement and Reliability
Takx, R. A. P., de Jong, P. A., Leiner, T., Oudkerk, M., de Koning, H. J., Mol, C. P., Viergever, M. A., & Išgum, I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091239 · Cited by 128 · see citing works
Self-citationWolterink, J. M., Leiner, T., Takx, R. A. P., Viergever, M. A., & Išgum, I. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2412651 · Cited by 119 · see citing works
Self-citationIšgum, I., Rutten, A., Prokop, M., & van Ginneken, B. (2007) · Medical Physics · doi:10.1118/1.2710548 · Cited by 106 · see citing works
Self-citationTakasu, J., Katz, R., Nasir, K., Carr, J. J., Wong, N. D., Detrano, R., & Budoff, M. J. (2008) · American Heart Journal · doi:10.1016/j.ahj.2007.11.019 · Cited by 106 · see citing works
Self-citationAutomated Agatston score computation in non-ECG gated CT scans using deep learning
González, G., Washko, G. R., Estépar, R. S. J., Cazorla, M., & Cano-Espinosa, C. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2293681 · Cited by 87 · see citing works
Vessel Specific Coronary Artery Calcium Scoring
Shahzad, R., van Walsum, T., Schaap, M., Rossi, A., Klein, S., Weustink, A. C., de Feyter, P. J., A, L. J. V. V., & Niessen, W. J. (2012) · Academic Radiology · doi:10.1016/j.acra.2012.07.018 · Cited by 81 · see citing works
Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT
Išgum, I., de Vos, B. D., Wolterink, J. M., Dey, D., Berman, D. S., Rubeaux, M., Leiner, T., & Slomka, P. J. (2017) · Journal of Nuclear Cardiology · doi:10.1007/s12350-017-0866-3 · Cited by 66 · see citing works
Self-citationA supervised classification-based method for coronary calcium detection in non-contrast CT
Kurkure, U., Chittajallu, D. R., Brunner, G., Le, Y. H., & Kakadiaris, I. A. (2010) · International journal of cardiac imaging · doi:10.1007/s10554-010-9607-2 · Cited by 62 · see citing works
Mylonas, I., Kazmi, M. H., Fuller, L., deKemp, R. A., Yam, Y., Chen, L., Beanlands, R. S., & Chow, B. J. W. (2012) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jes079 · Cited by 54 · see citing works
Sevrukov, A., Bland, M., & Kondos, G. T. (2005) · American Journal of Roentgenology · doi:10.2214/ajr.04.1589 · Cited by 50 · see citing works
Gernaat, S. A. M., Išgum, I., de Vos, B. D., Takx, R. A. P., Young‐Afat, D. A., Rijnberg, N., Grobbee, D. E., van der Graaf, Y., de Jong, P. A., Leiner, T., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167925 · Cited by 50 · see citing works
Self-citationAutomated aortic calcium scoring on low-dose chest computed tomography.
Išgum, I., Rutten, A., Prokop, M., Staring, M., Klein, S., Pluim, J., Viergever, M., & van Ginneken, B. (2010) · Medical Physics (Lancaster) · doi:10.1118/1.3284211 · Cited by 44 · see citing works
Self-citationGernaat, S. A. M., van Velzen, S. G. M., Koh, V., Emaus, M. J., Išgum, I., Leßmann, N., Moes, S. L., Jacobson, A., Tan, P. W., Grobbee, D. E., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.04.011 · Cited by 43 · see citing works
Self-citationSung, J. H., Yeboah, J., Lee, J. C., Smith, C. L., Terry, J. G., Sims, M., Samdarshi, T. E., Musani, S. K., Fox, E. R., Ge, Y., et al. (2015) · Journal of Advances in Medicine and Medical Research · doi:10.9734/bjmmr/2016/21449 · Cited by 32 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Adams, S. J., Stone, E., Baldwin, D. R., Vliegenthart, R., Lee, P., & Fintelmann, F. J. (2023) · The Lancet · doi:10.1016/s0140-6736(22)01694-4 · Cited by 428 · see citing works
Machine Learning and the Future of Cardiovascular Care
Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 346 · see citing works
Deep convolutional neural networks to predict cardiovascular risk from computed tomography
Zeleznik, R., Foldyna, B., Eslami, P., Weiss, J., Alexander, I., Taron, J., Parmar, C., Alvi, R. M., Banerji, D., Uno, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20966-2 · Cited by 240 · see citing works
Automated coronary calcium scoring using deep learning with multicenter external validation
Eng, D. K.-M., Chute, C., Khandwala, N., Rajpurkar, P., Long, J., Shleifer, S., Khalaf, M. H., Sandhu, A. T., Rodríguez, F., Maron, D. J., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00460-1 · Cited by 185 · see citing works
Eche, T., Schwartz, L. H., Mokrane, F.-Z., & Dercle, L. (2021) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2021210097 · Cited by 163 · see citing works
Artificial Intelligence for Cardiovascular Care—Part 1: Advances
Elias, P. A., Jain, S. S., Poterucha, T. J., Randazzo, M. J., Jimenez, F. L., Khera, R., Perez, M. V., Ouyang, D., Pirruccello, J. P., Salerno, M., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.03.400 · Cited by 134 · see citing works
Environmental Sustainability and AI in Radiology: A Double-Edged Sword
Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 124 · see citing works
Jafari, M., Shoeibi, A., Khodatars, M., Ghassemi, N., Moridian, P., Alizadehsani, R., Khosravi, A., Ling, S. H., Delfan, N., Zhang, Y., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106998 · Cited by 119 · see citing works
Coronary Artery Calcium Scoring: Current Status and Future Directions
Gupta, A. B., Bera, K., Kikano, E. G., Pierce, J. D., Gan, J. M., Rajdev, M. A., Ciancibello, L. M., Gupta, A., Rajagopalan, S., & Gilkeson, R. (2022) · Radiographics · doi:10.1148/rg.210122 · Cited by 110 · see citing works
Föllmer, B., Williams, M. C., Dey, D., Arbab‐Zadeh, A., Maurovich‐Horvat, P., Volleberg, R. H. J. A., Rueckert, D., Schnabel, J. A., Newby, D. E., Dweck, M. R., et al. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-023-00900-3 · Cited by 102 · see citing works
Self-citationArtificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease
Lin, A., Kolossváry, M., Motwani, M., Išgum, I., Maurovich‐Horvat, P., Slomka, P. J., & Dey, D. (2021) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2021200512 · Cited by 101 · see citing works
Self-citationGal, R., van Velzen, S. G. M., Hooning, M. J., Emaus, M. J., van der Leij, F., Gregorowitsch, M. L., Blezer, E. L. A., Gernaat, S. A. M., Leßmann, N., Sattler, M. G. A., et al. (2021) · JAMA Oncology · doi:10.1001/jamaoncol.2021.1144 · Cited by 90 · see citing works
Self-citationLiu, H., Chen, Y., Zhang, Y., Wang, L., Luo, R., Wu, H., Wu, C., Zhang, H., Tan, W., Yin, H., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07659-y · Cited by 88 · see citing works
Artificial intelligence in cardiac radiology
van Assen, M., Muscogiuri, G., Caruso, D., Lee, S., Laghi, A., & Cecco, C. N. D. (2020) · La radiologia medica · doi:10.1007/s11547-020-01277-w · Cited by 87 · see citing works
Slart, R. H. J. A., Williams, M. A., Juárez‐Orozco, L. E., Rischpler, C., Dweck, M. R., Glaudemans, A. W. J. M., Gimelli, A., Georgoulias, P., Gheysens, O. S., Gaemperli, O., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05341-z · Cited by 86 · see citing works
Self-citationCalcium Scoring at Coronary CT Angiography Using Deep Learning
Mu, D., Bai, J., Chen, W., Yu, H., Liang, J., Yin, K., Li, H., Zhao, Q., He, K., Yang, H., et al. (2021) · Radiology · doi:10.1148/radiol.2021211483 · Cited by 82 · see citing works
Pieszko, K., Shanbhag, A. D., Killekar, A., Miller, R. J. H., Lemley, M. H., Otaki, Y., Singh, A., Kwieciński, J., Gransar, H., Kriekinge, S. D. V., et al. (2022) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2022.06.006 · Cited by 82 · see citing works
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Wehbe, R. M., Katsaggelos, A. K., Hammond, K. J., Hong, H., Ahmad, F. S., Ouyang, D., Shah, S. J., McCarthy, P. M., & Thomas, J. D. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.3142 · Cited by 71 · see citing works
Mastrodicasa, D., van Assen, M., Huisman, M., Leiner, T., Williamson, E. E., Nicol, E. D., Allen, B. D., Saba, L., Vliegenthart, R., & Hanneman, K. (2025) · Radiology · doi:10.1148/radiol.240516 · Cited by 69 · see citing works
Self-citationLee, J., Kim, H., Kang, H. J., Koo, H. J., Kang, J., Kim, Y., & Yang, D. H. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2021.0148 · Cited by 65 · see citing works
Artificial intelligence in computed tomography plaque characterization: A review
Cau, R., Flanders, A. E., Mannelli, L., Politi, C., Faa, G., Suri, J. S., & Saba, L. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109767 · Cited by 62 · see citing works
Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning
Gogin, N., Viti, M., Nicodème, L., Ohana, M., Talbot, H., Gencer, U., Mekukosokeng, M., Caramella, T., Diascorn, Y., Airaud, J.-Y., et al. (2021) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2021.05.004 · Cited by 55 · see citing works
Artificial intelligence in cardiovascular CT: Current status and future implications
Lin, A., Kolossváry, M., Motwani, M., Išgum, I., Maurovich‐Horvat, P., Slomka, P. J., & Dey, D. (2021) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2021.03.006 · Cited by 54 · see citing works
Self-citationArtificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis
Muscogiuri, G., van Assen, M., Tesche, C., Cecco, C. N. D., Chiesa, M., Scafuri, S., Guglielmo, M., Baggiano, A., Fusini, L., Guaricci, A. I., et al. (2020) · BioMed Research International · doi:10.1155/2020/6649410 · Cited by 53 · see citing works
Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects
Haq, I. U., Chhatwal, K., SANAKA, K. O., & Xu, B. (2022) · Vascular Health and Risk Management · doi:10.2147/vhrm.s279337 · Cited by 61 · see citing works
Winkel, D. J., Suryanarayana, V., Ali, A. M., Görich, J., Buss, S. J., Mendoza, A., Schwemmer, C., Sharma, P. K., Schoepf, U. J., & Rapaka, S. (2021) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeab119 · Cited by 53 · see citing works
Tatsugami, F., Nakaura, T., Yanagawa, M., Fujita, S., Kamagata, K., Ito, R., Kawamura, M., Fushimi, Y., Ueda, D., Matsui, Y., et al. (2023) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2023.06.011 · Cited by 51 · see citing works
Schulze, K., Stantien, A.-M., Williams, M. C., Vassiliou, V. S., Giannopoulos, A. A., Nieman, K., Maurovich‐Horvat, P., Tarkin, J. M., Vliegenthart, R., Weir-McCall, J. R., et al. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01191-6 · Cited by 56 · see citing works
Self-citationInfante, T., Cavaliere, C., Punzo, B., Grimaldi, V., Salvatore, M., & Claudio Napoli, A. (2021) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.121.013025 · Cited by 50 · see citing works
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Vliegenthart, R., Fouras, A., Jacobs, C., & Papanikolaou, N. K. (2022) · Respirology · doi:10.1111/resp.14344 · Cited by 50 · see citing works
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Muscogiuri, G., Volpato, V., Cau, R., Chiesa, M., Saba, L., Guglielmo, M., Senatieri, A., Chierchia, G., Pontone, G., Dell’Aversana, S., et al. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10872 · Cited by 50 · see citing works
Baeßler, B., GOETZ, M. A., Antoniades, C. A., Heidenreich, J. F., Leiner, T., & Beer, M. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1120361 · Cited by 52 · see citing works
Self-citationIhdayhid, A. R., Lan, N. S. R., Williams, M. C., Newby, D. E., Flack, J., Kwok, S., Joyner, J., Gera, S., Dembo, L. G., Adler, B. D., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09028-3 · Cited by 48 · see citing works
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Abdelrahman, K., Shiyovich, A., Huck, D. M., Berman, A. N., Weber, B., Gupta, S., Cardoso, R. N., & Blankstein, R. (2024) · Diagnostics · doi:10.3390/diagnostics14020125 · Cited by 46 · see citing works
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Tian, Y., & Fu, S. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106445 · Cited by 45 · see citing works
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Miller, R. J. H., Killekar, A., Shanbhag, A., Bednarski, B., Michalowska, A. M., Ruddy, T. D., Einstein, A. J., Newby, D. E., Lemley, M., Pieszko, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46977-3 · Cited by 44 · see citing works
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Gautam, N., Saluja, P., Malkawi, A., Rabbat, M., Al-Mallah, M. H., Pontone, G., Zhang, Y., Lee, B. C., & Alaref, S. J. (2022) · Healthcare · doi:10.3390/healthcare10020232 · Cited by 43 · see citing works
Slomka, P. J., Miller, R. J. H., Išgum, I., & Dey, D. (2020) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2020.03.004 · Cited by 42 · see citing works
Self-citationDai, L., Zhou, Q., Zhou, H., Zhang, H., Cheng, P., Ding, M., Xu, X., & Zhang, X. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109528 · Cited by 39 · see citing works
Artificial Intelligence and Machine Learning in Lung Cancer Screening
Adams, S., Mikhael, P. G., Wohlwend, J., Barzilay, R., Sequist, L. V. D., & Fintelmann, F. J. (2023) · Thoracic surgery clinics/Thorac. surg. clin. · doi:10.1016/j.thorsurg.2023.03.001 · Cited by 38 · see citing works
Williams, M. C., Weir-McCall, J. R., Baldassarre, L. A., Cecco, C. N. D., Choi, A. D., Dey, D., Dweck, M. R., Išgum, I., Kolossváry, M., Leipsic, J. A., et al. (2024) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2024.08.003 · Cited by 38 · see citing works
Self-citationSuh, Y. J., Kim, C., Lee, J., Oh, H., Kang, H., Kim, Y., & Yang, D. H. (2022) · European Radiology · doi:10.1007/s00330-022-09117-3 · Cited by 37 · see citing works
Machine Learning and Coronary Artery Calcium Scoring
Lee, H., Martin, S. S., Burt, J. R., Sahbaee, P., Rapaka, S., Gray, H. N., Leonard, T. J., Schwemmer, C., & Schoepf, U. J. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01337-7 · Cited by 37 · see citing works
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Gupta, V., Erdal, B. S., Ramirez, C., Floca, R. O., Genereaux, B. W., Bryson, S., Bridge, C. P., Kleesiek, J., Nensa, F., Braren, R., et al. (2024) · JMIR AI · doi:10.2196/55833 · Cited by 35 · see citing works
Roguin, A., Dogosh, A. A., Feld, Y., Konigstein, M., Lerman, A., & Koifman, E. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2020.10.022 · Cited by 35 · see citing works
Greniér, P., Brun, A., & Mellot, F. (2022) · Diagnostics · doi:10.3390/diagnostics12102435 · Cited by 35 · see citing works
Han, D., Klein, E., Friedman, J. D., Gransar, H., Achenbach, S. S., Al-Mallah, M. H., Budoff, M. J., Cademartiri, F., Maffei, E., Callister, T. Q., et al. (2020) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2020.07.011 · Cited by 32 · see citing works
Artificial intelligence in atherosclerotic disease: Applications and trends
Kampaktsis, P. Ν., Emfietzoglou, M., Shehhi, A. A., Fasoula, N., Bakogiannis, C., Mouselimis, D., Tsarouchas, A., Vassilikos, V. P., Kallmayer, M. A., Eckstein, H., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.949454 · Cited by 32 · see citing works
Current and Future Applications of Artificial Intelligence in Cardiac CT
Joshi, M. M., Melo, D. P., Ouyang, D., Slomka, P. J., Williams, M. C., & Dey, D. (2023) · Current Cardiology Reports · doi:10.1007/s11886-022-01837-8 · Cited by 32 · see citing works
van Velzen, S. G. M., Gal, R., Teske, A. J., van der Leij, F., van den Bongard, H. J. G. D., Viergever, M. A., Verkooijen, H. M., & Išgum, I. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.09.008 · Cited by 30 · see citing works
Self-citationAtkins, K. M., Weiss, J., Zeleznik, R., Bitterman, D. S., Chaunzwa, T. L., Huynh, E., Guthier, C., Kozono, D. E., Lewis, J. H., Tamarappoo, B. K., et al. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00095 · Cited by 30 · see citing works
Machine Learning in Nuclear Medicine: Part 2—Neural Networks and Clinical Aspects
Zukotynski, K., Gaudet, V., Uribe, C., Mathotaarachchi, S. S., Smith, K. C., Rosa‐Neto, P., Bénard, F., & Black, S. E. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.119.231837 · Cited by 29 · see citing works
Deep learning–based atherosclerotic coronary plaque segmentation on coronary CT angiography
Jávorszky, N., Homonnay, B., Gerstenblith, G., Bluemke, D. A., Kiss, P., Török, M., Celentano, D. D., Lai, H. C., Lai, S., & Kolossváry, M. (2022) · European Radiology · doi:10.1007/s00330-022-08801-8 · Cited by 29 · see citing works
Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
Lee, S., Rim, B., Jou, S.-S., Gil, H., Jia, X., Lee, A., & Hong, M. (2021) · Sensors · doi:10.3390/s21217059 · Cited by 28 · see citing works
Walls, G., Giacometti, V., Apte, A., Thor, M., McCann, C. J., Hanna, G. G., O’Connor, J. D., Deasy, J. O., Hounsell, A. R., Butterworth, K. T., et al. (2022) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2022.07.003 · Cited by 28 · see citing works
Artificial intelligence in cardiac computed tomography
Aromiwura, A. A., Settle, T., Umer, M., Joshi, J. K., Shotwell, M., Mattumpuram, J., Vorla, M., Sztukowska, M. N., Contractor, S. G., Amini, A. A., et al. (2023) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2023.09.001 · Cited by 29 · see citing works
Application of Artificial Intelligence to Cardiovascular Computed Tomography
Yang, D. H. (2021) · Korean Journal of Radiology · doi:10.3348/kjr.2020.1314 · Cited by 26 · see citing works
Eberhard, M., Hinzpeter, R., Schönenberger, A., Euler, A., Kuzo, N., Reeve, K. A., Stähli, B. E., Kasel, A. M., Manka, R., Tanner, F. C., et al. (2021) · Radiology · doi:10.1148/radiol.2021204623 · Cited by 26 · see citing works
Kang, H. W., Ahn, W. J., Jeong, J. H., Suh, Y. J., Yang, D. H., Choi, H., Hwang, S. H., Yong, H. S., Oh, Y., Kang, E.-Y., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09143-1 · Cited by 25 · see citing works
Yang, W., Chen, C., Yang, Y., Chen, L., Yang, C., Gong, L., Wang, J., Shi, F., Wu, D., & Yan, F. (2023) · La radiologia medica · doi:10.1007/s11547-023-01606-9 · Cited by 25 · see citing works
Ischemia and outcome prediction by cardiac CT based machine learning
Brandt, V., Emrich, T., Schoepf, U. J., Dargis, D. M., Bayer, R. R., Cecco, C. N. D., & Tesche, C. (2020) · International journal of cardiac imaging · doi:10.1007/s10554-020-01929-y · Cited by 23 · see citing works
Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline
Zheng, Y., Chen, Z., Huang, S., Zhang, N., Wang, Y., Hong, S., Chan, J. S. K., Chen, K., Xia, Y., Zhang, Y., et al. (2023) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2410296 · Cited by 23 · see citing works
Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease
Chen, S., Loguercio, S., Chen, K., Lee, S. E., Park, J., Liu, S., Sadaei, H. J., & Torkamani, A. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00731-4 · Cited by 24 · see citing works
Xu, C., Guo, H., Xu, M., Duan, M., Wang, M., Liu, P., Luo, X., Jin, Z., Liu, H., & Wang, Y. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1017 · Cited by 22 · see citing works
van den Oever, L. B., Cornelissen, L. J., Vonder, M., Xia, C., van Bolhuis, J. N., Vliegenthart, R., Veldhuis, R. N. J., de Bock, G. H., Oudkerk, M., & van Ooijen, P. M. A. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109114 · Cited by 21 · see citing works
Yu, J., Qian, L., Sun, W., Nie, Z., Zheng, D., Han, P., Shi, H., Zheng, C., & Yang, F. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00907-1 · Cited by 21 · see citing works
Precision cardiovascular medicine: shifting the innovation paradigm
Aikawa, M., Sonawane, A. R., Chelvanambi, S., Asano, T., Halu, A., Matamalas, J. T., Singh, S. A., Uchida, S., Aïkawa, E., Arenas, À., et al. (2025) · Frontiers in Science · doi:10.3389/fsci.2025.1474469 · Cited by 22 · see citing works
Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT
Homayounieh, F., Yan, P., Digumarthy, S. R., Krüger, U., Wang, G., & Kalra, M. K. (2021) · Academic Radiology · doi:10.1016/j.acra.2020.09.021 · Cited by 20 · see citing works
Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies
Juárez‐Orozco, L. E., Klén, R., Niemi, M., Ruijsink, B., Daquarti, G. J., van Es, R., Benjamins, J. W., Yeung, M. W., van der Harst, P., & Knuuti, J. M. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01649-w · Cited by 20 · see citing works
Machine Learning in Cardiovascular Imaging
Kagiyama, N., Tokodi, M., & Sengupta, P. P. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.003 · Cited by 20 · see citing works
Dekker, M., Waissi, F., Bank, I. E. M., Išgum, I., Scholtens, A. M., Velthuis, B. K., Pasterkamp, G., de Winter, R. J., Mosterd, A., Timmers, L., et al. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.12.079 · Cited by 20 · see citing works
Self-citationThe application of artificial intelligence to chest medical image analysis
Liu, F., Tang, J., Ma, J., Wang, C., Ha, Q., Yu, Y., & Zhou, Z. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.06.004 · Cited by 19 · see citing works
Tharmaseelan, H., Froelich, M. F., Nörenberg, D., Overhoff, D., Rotkopf, L. T., Riffel, P., Schoenberg, S. O., & Ayx, I. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-022-02656-2 · Cited by 18 · see citing works
Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification
Lin, A., Pieszko, K., Park, C., Ignor, K., Williams, M. C., Slomka, P. J., & Dey, D. (2023) · BJR|Open · doi:10.1259/bjro.20220021 · Cited by 18 · see citing works
Maragna, R., Giacari, C. M., Guglielmo, M., Baggiano, A., Fusini, L., Guaricci, A. I., Rossi, A., Rabbat, M., & Pontone, G. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.736223 · Cited by 19 · see citing works
Active multitask learning with uncertainty‐weighted loss for coronary calcium scoring
Föllmer, B., Biavati, F., Wald, C., Stober, S., Ma, J., Dewey, M., & Samek, W. (2022) · Medical Physics · doi:10.1002/mp.15870 · Cited by 17 · see citing works
Canan, A., Ghandour, A., Saboo, S. S., & Rajiah, P. S. (2023) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt-23-79 · Cited by 17 · see citing works
Artificial Intelligence in the Management of Heart Failure
Cheema, B. S., Hourmozdi, J., Kline, A. S., Ahmad, F. S., & Khera, R. (2025) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2025.02.020 · Cited by 16 · see citing works
AI Opportunistic Coronary Calcium Screening at Veterans Affairs Hospitals
Hagopian, R., Strebel, T., Bernatz, S., Myers, G. A., Offerman, E. J., Zuniga, E., Kim, C. Y., Ng, A. T., Iwaz, J. A., Nürnberg, L., et al. (2025) · NEJM AI · doi:10.1056/aioa2400937 · Cited by 16 · see citing works
Zhou, Z. X., Gao, Y., Zhang, W., Bo, K., Zhang, N., Wang, H., Wang, R., Du, Z., Firmin, D. N., Yang, G., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08975-1 · Cited by 16 · see citing works
Chen, Q., Zhou, F., Xie, G., Tang, C. X., Gao, X., Zhang, Y., Yin, X., Xu, H., & Zhang, L. J. (2024) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2501027 · Cited by 16 · see citing works
Gennari, A. G., Rossi, A., De Cecco, C. N., van Assen, M., Sartoretti, T., Giannopoulos, A. A., Schwyzer, M., Huellner, M. W., & Messerli, M. (2024) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-024-03080-4 · Cited by 16 · see citing works
Mousseaux, É., Fayol, A., Danchin, N., Soulat, G., Charpentier, E., Livrozet, M., Carvès, J.-B., Tea, V., Salem, F. B., Chamandi, C., et al. (2021) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2021.06.007 · Cited by 16 · see citing works
Sartoretti, T., Gennari, A. G., Sartoretti, E., Skawran, S., Maurer, A. B., Buechel, R. R., & Messerli, M. (2022) · Journal of Nuclear Cardiology · doi:10.1007/s12350-022-02940-7 · Cited by 16 · see citing works
Guilenea, F. N., Casciaro, M. E., Pascaner, A. F., Soulat, G., Mousseaux, É., & Craiem, D. O. (2021) · Tomography · doi:10.3390/tomography7040054 · Cited by 15 · see citing works
Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes
Tahir, A., Mutlu, O., Bensaali, F., Ward, R., Ghareeb, A., Helmy, S., Othman, K., Al-Hashemi, M., Abujalala, S., Chowdhury, M. E. H., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12144774 · Cited by 12 · see citing works
Choi, J. H., Cha, M. J., Cho, I., Kim, W. D., Ha, Y., Choi, H., Lee, S. H., You, S. C., & Chang, J. S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.989250 · Cited by 15 · see citing works
The Evolving Role of Artificial Intelligence in Cardiac Image Analysis
Thériault-Lauzier, P., Avram, R., Dey, D., Slomka, P. J., Afilalo, J., & Chow, B. J. W. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.09.030 · Cited by 15 · see citing works
van Velzen, S. G. M., Dobrolińska, M. M., Knaapen, P., van Herten, R. L. M., Jukema, R., Danad, I., Slart, R. H. J. A., Greuter, M. J. W., & Išgum, I. (2022) · Journal of Nuclear Cardiology · doi:10.1007/s12350-022-03047-9 · Cited by 14 · see citing works
Self-citationThe Role of Artificial Intelligence in Cardiac Imaging
Onnis, C., van Assen, M., Muscogiuri, E., Muscogiuri, G., Gershon, G., Saba, L., & Cecco, C. N. D. (2024) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2024.01.002 · Cited by 14 · see citing works
Wang, T.-W., Tzeng, Y.-H., Wu, K.-T., Liu, H.-R., Hong, J.-S., Hsu, H.-Y., Fu, H.-N., Lee, Y.-T., Yin, W.-H., & Wu, Y.-T. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109295 · Cited by 14 · see citing works
Big data and new information technology: what cardiologists need to know
Baladrón, C., de Diego, J. J. G., & Amat-Santos, I. J. (2020) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2020.06.036 · Cited by 14 · see citing works
Cardiovascular CT and MRI in 2020: Review of Key Articles
Gulsin, G. S., McVeigh, N., Leipsic, J. A., & Dodd, J. D. (2021) · Radiology · doi:10.1148/radiol.2021211002 · Cited by 14 · see citing works
Wilder-Smith, A. J., Yang, S., Weikert, T., Bremerich, J., Haaf, P., Segeroth, M., Ebert, L. C., Sauter, A. W., & Sexauer, R. (2022) · Diagnostics · doi:10.3390/diagnostics12051045 · Cited by 14 · see citing works
Coronary Artery Calcium Scoring on Dedicated Cardiac CT and Noncardiac CT Scans
Dzaye, O. D. A., Razavi, A. C., Jelwan, Y. A., Peng, A. W., Grant, J. K., & Blaha, M. J. (2025) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.240548 · Cited by 13 · see citing works
Refsgaard, L. H., Milo, M. L. H., Buhl, E. S., Jensen, J. M., Maae, E., Berg, M., Jensen, I., Nielsen, M. H., Lorenzen, E. L., Thorsen, L. B. J., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110705 · Cited by 13 · see citing works
Benjamins, J. W., Yeung, M. W., Maaniitty, T., Saraste, A., Klén, R., van der Harst, P., Knuuti, J. M., & Juárez‐Orozco, L. E. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.04.009 · Cited by 13 · see citing works
Deep Learning-Based Long Term Mortality Prediction in the National Lung Screening Trial
Lu, Y., Aslani, S., Emberton, M., Alexander, D. C., & Jacob, J. (2022) · IEEE Access · doi:10.1109/access.2022.3161954 · Cited by 13 · see citing works
Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis
Alskaf, E., Dutta, U., Scannell, C. M., & Chiribiri, A. (2022) · Journal of Medical Artificial Intelligence · doi:10.21037/jmai-22-36 · Cited by 12 · see citing works
Yamaoka, T., & Watanabe, S. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110855 · Cited by 15 · see citing works
- Authors
- van Velzen, S. G. M., Lessmann, N., Velthuis, B. K., Bank, I. E. M., van den Bongard, D. H. J. G., Leiner, T., de Jong, P. A., Veldhuis, W. B., Correa, A., Terry, J. G., Carr, J. J., Viergever, M. A., Verkooijen, H. M., & Išgum, I.
- Affiliations
- Radboud University Medical Center (NL)
- ORCID
- Venue
- Radiology
- Published
- 2020-04
- Volume
- 295
- Issue
- 1
- Pages
- 66-79
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7106943
- OpenAlex
- W3005949594
- Cited by
- 244
- References
- 34
- Citations by year
- 2026: 29 · 2025: 26 · 2024: 40 · 2023: 36 · 2022: 48 · 2021: 47 · 2020: 18
- FWCI
- 18.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- U.S. Department of Health and Human Services (HHSN268201800014I, HHSN268201800010I, HHSN268201800013I, HHSN268201800012I, HHSN268201800015I)
- Jackson State University (HHSN268201800015I/HHSN26800001, HHSN268201800013 I, HHSN268201800014 I)
- Mississippi State Department of Health (HHSN26800001, HHSN268201800015 I, HHSN268201800015I/HHSN26800001)
- National Institutes of Health (HHSN268201800010I, HHSN268201800012I, HHSN268201800014I, HHSN26800001, HHSN268201800013I, HHSN268201800015I)
- National Heart, Lung, and Blood Institute (HHSN268201800013I, HHSN26800001, HHSN268201800012C, HHSN268201800010 I, HHSN268201800014 I, HHSN268201800012 I, HHSN268201100011I, HHSN268201800014C, HHSN268201800015I, HHSN268201800011C, HHSN268201800011I)
- National Cancer Institute (HHSN261201800014I, HHSN261201800012I, HHSN261201800010I)
- National Institute on Minority Health and Health Disparities (HHSN268201800012I, HHSN268201800010I, HHSN268201800014I, HHSN268201800015I, HHSN268201800013I)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- coronary artery calcium scoring, thoracic aortic calcification, deep learning, convolutional neural networks, chest CT, low-dose CT, cardiovascular risk stratification, Agatston score, cardiovascular risk prediction, cardiovascular risk prediction models
- MeSH terms
- Deep Learning, Aged, Clinical Protocols, Coronary Artery Disease, Female, Heart, Humans, Male, Middle Aged, Retrospective Studies, Thorax, Tomography, X-Ray Computed, Vascular Calcification
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