Petersen, Steffen E.

2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure

McDonagh, T. A., Metra, M., Adamo, M., Gardner, R. S., Baumbach, A., Böhm, M., Burri, H., Butler, J., Čelutkienė, J., Chioncel, O., et al. (2021) · European Heart Journal

Heart Failure Treatment and ManagementCardiovascular Function and Risk Factors

Cited by 13,344

2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes

Knuuti, J., Wijns, W., Saraste, A., Capodanno, D., Barbato, E., Funck-Brentano, C., Prescott, E., Storey, R. F., Deaton, C., Cuisset, T., et al. (2020) · European Heart Journal

Coronary Interventions and DiagnosticsAcute Myocardial Infarction Research

Cited by 7,166

2021 ESC Guidelines on cardiovascular disease prevention in clinical practice

Visseren, F. L. J., Mach, F., Smulders, Y. M., Carballo, D., Koskinas, K. C., Bäck, M., Benetos, A., Biffi, A., Boavida, J.-M., Capodanno, D., et al. (2021) · European Heart Journal

Health Promotion and Cardiovascular PreventionClinical practice guidelines implementation

Cited by 6,485

2023 ESC Guidelines for the management of acute coronary syndromes

Byrne, R. A., Rossello, X., Coughlan, J. J., Barbato, E., Berry, C., Chieffo, A., Claeys, M. J., Dan, G.-A., Dweck, M. R., Galbraith, M., et al. (2023) · European Heart Journal

Acute Myocardial Infarction ResearchCoronary Interventions and Diagnostics

Cited by 5,333

2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation

Collet, J.-P., Thiele, H., Barbato, E., Barthélémy, O., Bauersachs, J., Bhatt, D. L., Dendale, P., Dorobantu, M., Edvardsen, T., Folliguet, T., et al. (2021) · European Heart Journal

Acute Myocardial Infarction ResearchCardiac Imaging and Diagnostics

Cited by 5,037

2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk

Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Casula, M., Badimon, L., Chapman, M. J., De Backer, G. G., Delgado, V., Ference, B. A., et al. (2019) · Atherosclerosis

Lipoproteins and Cardiovascular HealthDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 4,854

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 2,431

2020 ESC guidelines on sports cardiology and exercise in patients with cardiovascular disease

Pelliccia, A., Sharma, S., Gati, S., Bäck, M., Börjesson, M., Caselli, S., Collet, J.-P., Corrado, D., Drezner, J. A., Halle, M., et al. (2021) · European Heart Journal

Cardiovascular Effects of ExerciseCardiovascular and exercise physiology

Cited by 1,958

2023 ESC guidelines for the management of cardiovascular disease in patients with diabetes.

Marx, N., Federici, M., Schütt, K., Müller-Wieland, D., Ajjan, R. A., Antunes, M. J., Christodorescu, R. M., Crawford, C., Di Angelantonio, E., Eliasson, B., et al. (2023) · European Heart Journal

Diabetes Treatment and ManagementDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 1,642

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

Salih, A. M., Raisi‐Estabragh, Z., Galazzo, I. B., Radeva, P., Petersen, S. E., Lekadir, K., & Menegaz, G. (2025) · Advanced Intelligent Systems

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 897

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

Littlejohns, T. J., Holliday, J., Gibson, L. M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., Bell, J. D., Boultwood, C., Collins, R., Conroy, M. C., et al. (2020) · Nature Communications

Advanced MRI Techniques and ApplicationsHealth, Environment, Cognitive Aging

Cited by 812

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 773

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Campello, V. M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P. M., Maier-Hein, K., Zhang, Y., He, Z., Ma, J., et al. (2021) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 481

UK Biobank's cardiovascular magnetic resonance protocol

Petersen, S. E., Matthews, P. M., Francis, J. M., Robson, M. D., Zemrak, F., Boubertakh, R., Young, A. A., Hudson, S., Weale, P., Garratt, S., et al. (2016) · Journal of Cardiovascular Magnetic Resonance

Cardiac Imaging and DiagnosticsAdvanced MRI Techniques and Applications

Cited by 481

Prior SARS-CoV-2 infection rescues B and T cell responses to variants after first vaccine dose

Reynolds, C. J., Pade, C., Gibbons, J. M., Butler, D. K., Otter, A. D., Menacho, K., Fontana, M., Smit, A., Sackville-West, J. E., Cutino-Moguel, T., et al. (2021) · Science

SARS-CoV-2 and COVID-19 ResearchImmune responses and vaccinations

Cited by 338

Obesity and cardiovascular disease: An ESC clinical consensus statement

Koskinas, K. C., Van Craenenbroeck, E. M., Antoniades, C., Blüher, M., Gorter, T. M., Hanssen, H., Marx, N., McDonagh, T. A., Mingrone, G., Rosengren, A., et al. (2024) · European Heart Journal

Cardiovascular Function and Risk FactorsObesity, Physical Activity, Diet

Cited by 313

The science of clinical practice: disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice

Croft, P., Altman, D. G., Deeks, J. J., Dunn, K. M., Hay, A. D., Hemingway, H., LeResche, L., Peat, G., Perel, P., Petersen, S. E., et al. (2015) · BMC Medicine

Clinical Reasoning and Diagnostic SkillsMedicine, History, and Philosophy

Cited by 250

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation

Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine

Artificial Intelligence in Healthcare and EducationMedical Image Segmentation Techniques

Cited by 85

Medical image segmentation automatic quality control: A multi-dimensional approach

Fournel, J., Bartoli, A., Bendahan, D., Guye, M., Bernard, M., Rauseo, E., Khanji, M. Y., Petersen, S. E., Jacquier, A., & Ghattas, B. (2021) · Medical Image Analysis

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 45

Minimising multi-centre radiomics variability through image normalisation: a pilot study

Campello, V. M., Martín-Isla, C., Izquierdo, C., Guala, A., Palomares, J. F. R., Viladés, D., Descalzo, M. L., Karakas, M., Çavuş, E., Raisi-Estabragh, Z., et al. (2022) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 24