Philipp A. Kaufmann

Active 1992–2025

213
Papers
30,556
Citations
99
h-index
204
i10-index

Citations

Citations per year for Philipp A. Kaufmann1950: 1 citations1991: 4 citations1993: 1 citations1995: 2 citations1996: 1 citations1997: 5 citations1998: 4 citations1999: 5 citations2000: 20 citations2001: 18 citations2002: 31 citations2003: 40 citations2004: 52 citations2005: 65 citations2006: 56 citations2007: 134 citations2008: 185 citations2009: 183 citations2010: 224 citations2011: 218 citations2012: 217 citations2013: 164 citations2014: 182 citations2015: 153 citations2016: 157 citations2017: 184 citations2018: 146 citations2019: 639 citations2020: 827 citations2021: 830 citations2022: 753 citations2023: 464 citations2024: 840 citations2025: 382 citations2026: 21 citations1951–1990: no citations, so these years are not shown1992: no citations, so this year is not shown1994: no citations, so this year is not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 1,915 citing papers, 19.2% of this breakdownItaly: 765 citing papers, 7.7% of this breakdownUnited Kingdom: 674 citing papers, 6.7% of this breakdownGermany: 657 citing papers, 6.6% of this breakdownNetherlands: 553 citing papers, 5.5% of this breakdownChina: 545 citing papers, 5.5% of this breakdownSwitzerland: 465 citing papers, 4.6% of this breakdownCanada: 459 citing papers, 4.6% of this breakdownFrance: 300 citing papers, 3% of this breakdownJapan: 274 citing papers, 2.7% of this breakdownAustralia: 236 citing papers, 2.4% of this breakdownSpain: 228 citing papers, 2.3% of this breakdown
0%19.2%Other 29.2%

Fields

  • Medicine86.9%
  • Engineering3.6%
  • Biochemistry, Genetics and Molecular Biology3.4%
  • Computer Science1.8%
  • Health Professions1.4%
  • Neuroscience0.6%
  • Other2.3%

Topics

  • Cardiac Imaging and Diagnostics15%
  • Cardiovascular Function and Risk Factors7.1%
  • Coronary Interventions and Diagnostics5%
  • Advanced X-ray and CT Imaging4.2%
  • Medical Imaging Techniques and Applications3.4%
  • Acute Myocardial Infarction Research3.1%
  • Other62.2%

Coauthors

All papers

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  1. Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: a consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Kristina H. Haugaa, Maurizio Galderisi, Nuno Cardim, Philipp A. Kaufmann, Pier Giorgio Masci, Nina Ajmone Marsan, Monica Roşca, Matteo Cameli, Leyla Elif Sade - European Heart Journal - Cardiovascular Imaging 2018 cited by 1,628

  2. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Leslee J. Shaw, Julia Stehli, Todd C. Villines, Allison Dunning, James K. Min, Piotr J. Slomka - European Heart Journal 2016 cited by 705

  3. Myocardial Perfusion PET for the Detection and Reporting of Coronary Microvascular Dysfunction

    Authors: , , , , , , , , , , , , , , , , , , , , , , - JACC. Cardiovascular imaging 2023 cited by 176

  4. When Does a Calcium Score Equate to Secondary Prevention?

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , - JACC. Cardiovascular imaging 2023 cited by 113

  5. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT

    Authors: , , , , , , , , , , , , , , , , , , , - JACC. Cardiovascular imaging 2018 cited by 348

  6. Age- and Sex-Related Differences in All-Cause Mortality Risk Based on Coronary Computed Tomography Angiography Findings

    Authors: , , , , , , , , , , , , , , , , , , , , - Journal of the American College of Cardiology 2011 cited by 768

  7. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease

    Authors: , , , , , , , , , , , , , , , , , , , , - JACC. Cardiovascular imaging 2021 cited by 115

  8. Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy

    Authors: , , , , , , , , , , , , - Journal of cardiovascular computed tomography 2020 cited by 170

  9. Optimized Prognostic Score for Coronary Computed Tomographic Angiography

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , - Journal of the American College of Cardiology 2013 cited by 272

  10. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Pedro de Araújo Gonçalves, Gianluca Pontone, Gilbert Raff, Ronen Rubinshtein, Todd C. Villines, Heidi Gransar, Yao Lu, Erica C. Jones, Jessica M. Peña, Fay Y. Lin, James K. Min, Leslee J. Shaw - European Heart Journal 2019 cited by 238

  11. Anatomic Versus Physiologic Assessment of Coronary Artery Disease

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , - Journal of the American College of Cardiology 2013 cited by 564

  12. Diagnostic performance of choline PET for detection of hyperfunctioning parathyroid glands in hyperparathyroidism: a systematic review and meta-analysis

    Authors: , , , , , , - European Journal of Nuclear Medicine and Molecular Imaging 2018 cited by 225

  13. Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Saïd Ghostine, Ronny R. Buechel, Konstantin Nikolaou, Hans Mickley, Lin Yang, Zhaqoi Zhang, Marcus Y. Chen, David A. Halon, Matthias Rief, Kai Sun, Beatrice Hirt-Moch, Hiroyuki Niinuma, Roy Marcus, Simone Muraglia, Réda Jakamy, Benjamin J.W. Chow, Philipp A. Kaufmann, Jean‐Claude Tardif, César Higa Nomura, Klaus F. Kofoed, Jean-Pierre Laissy, Armin Arbab‐Zadeh, Kakuya Kitagawa, Roger J. Laham, Masahiro Jinzaki, John Hoe, Frank J. Rybicki, Arthur J. Scholte, Narinder Paul, Swee Yaw Tan, Kunihiro Yoshioka, Robert Röhle, Georg M. Schuetz, Sabine Schueler, Maria H Coenen, Viktoria Wieske, Stephan Achenbach, Matthew J. Budoff, Michael Laule, David E. Newby, Marc Dewey - BMJ 2019 cited by 182

  14. Prevalence and Severity of Coronary Artery Disease and Adverse Events Among Symptomatic Patients With Coronary Artery Calcification Scores of Zero Undergoing Coronary Computed Tomography Angiography

    Authors: , , , , , , , , , , , , , , , , , , , , , , - Journal of the American College of Cardiology 2011 cited by 397

  15. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)

    Authors: , , , , , , , , , , , , , , , , , , , , - Journal of Nuclear Cardiology 2018 cited by 114

  16. Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography

    Authors: , , , , , , , , , , , - European Radiology 2021 cited by 74

  17. SNMMI/ASNC/SCCT Guideline for Cardiac SPECT/CT and PET/CT 1.0

    Authors: , , , , , , , , , , , , - Journal of Nuclear Medicine 2013 cited by 233

  18. Long-Term Prognostic Value of 13N-Ammonia Myocardial Perfusion Positron Emission Tomography

    Authors: , , , , , , , , - Journal of the American College of Cardiology 2009 cited by 615

  19. Prognostic value of coronary computed tomographic angiography findings in asymptomatic individuals: a 6-year follow-up from the prospective multicentre international CONFIRM study

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Hyuk‐Jae Chang, James K. Min - European Heart Journal 2017 cited by 136

  20. Deep Learning–Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT

    Authors: , , , , , , , , , , , , , , - Journal of Nuclear Medicine 2022 cited by 59

  21. Superior Risk Stratification With Coronary Computed Tomography Angiography Using a Comprehensive Atherosclerotic Risk Score

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Millie Gomez, Niree Hindoyan, Heidi Gransar, Jonathon Leipsic, Jagat Narula, James K. Min, Jeroen J. Bax - JACC. Cardiovascular imaging 2019 cited by 116

  22. Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Todd C. Villines, Heidi Gransar, Yao Lu, Jessica M. Peña, Fay Y. Lin, Leslee J. Shaw, James K. Min, Jeroen J. Bax - European Heart Journal - Cardiovascular Imaging 2020 cited by 82

  23. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

    Authors: , , , , , , , , , , , , , , , , , , , , , , , , - JACC. Cardiovascular imaging 2022 cited by 49

  24. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

    Authors: , , , , , , , , , , , , , , , , , , , , - npj Digital Medicine, npj Digit. Medicine 2023 cited by 27