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Results: 45
Number of items: 45
  • Open Access
    Williams, M. C., Weir-McCall, J. R., Baldassarre, L. A., De Cecco, C. N., Choi, A. D., Dey, D., Dweck, M. R., Isgum, I., Kolossvary, M., Leipsic, J., Lin, A., Lu, M. T., Motwani, M., Nieman, K., Shaw, L., van Assen, M., & Nicol, E. (2024). Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT). Journal of cardiovascular computed tomography, 18(6), 519–532. https://doi.org/10.1016/j.jcct.2024.08.003
  • Open Access
    Oudkerk Pool, M. D. (2024). Innovations in cardiology: Towards patient centered care. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van de Vijver, W. R., Hennecken, J., Lagogiannis, I., Pérez del Villar, C., Herrera, C., Douek, P. C., Segev, A., Hovingh, G. K., Išgum, I., Winter, M. M., Planken, R. N., & Claessen, B. E. P. M. (2024). The Role of Coronary Computed Tomography Angiography in the Diagnosis, Risk Stratification, and Management of Patients with Diabetes and Chest Pain. Reviews in Cardiovascular Medicine, 25(12), Article 442. https://doi.org/10.31083/j.rcm2512442
  • Open Access
    van Erck, D., Moeskops, P., Schoufour, J. D., Weijs, P. J. M., Scholte op Reimer, W. J. M., van Mourik, M. S., Planken, R. N., Vis, M. M., Baan, J., Išgum, I., Henriques, J. P., de Vos, B. D., & Delewi, R. (2024). Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis. Clinical Nutrition ESPEN, 63, 142–147. https://doi.org/10.1016/j.clnesp.2024.06.013
  • Open Access
    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., Guagliumi, G., Falk, V., Vázquez-Mézquita, A. J., Biavati, F., Išgum, I., & Dewey, M. (2024). Roadmap on the Use of Artificial Intelligence for Imaging of Vulnerable Atherosclerotic Plaque in Coronary Arteries. Nature Reviews. Cardiology, 21(1), 51-64. https://doi.org/10.1038/s41569-023-00900-3
  • Open Access
    van Herten, R. L. M., Lagogiannis, I., Leiner, T., & Išgum, I. (2024). The role of artificial intelligence in coronary CT angiography. Netherlands Heart Journal, 32(11), 417–425. https://doi.org/10.1007/s12471-024-01901-8
  • Open Access
    Hampe, N., van Velzen, S. G. M., Wolterink, J. M., Collet, C., Henriques, J. P. S., Planken, N., & Išgum, I. (2024). Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography. Journal of Medical Imaging, 11(03), Article 034001 . https://doi.org/10.1117/1.jmi.11.3.034001
  • Open Access
    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., Guagliumi, G., Falk, V., Vázquez-Mézquita, A. J., Biavati, F., Išgum, I., & Dewey, M. (2024). Roadmap on the Use of Artificial Intelligence for Imaging of Vulnerable Atherosclerotic Plaque in Coronary Arteries. In I. Sack, & T. Schaeffter (Eds.), Quantification of Biophysical Parameters in Medical Imaging (2nd ed., pp. 547–568). Springer. https://doi.org/10.1007/978-3-031-61846-8_27
  • Open Access
    Płotka, S. S. (2024). Enhancing prenatal care through deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sander, J. (2023). Assessing anatomy and function of the heart using 4D cardiac MRI and deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
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