K. Pieszko

发表

The risk for subsequent coronary interventions in a local Polish population

B. Musielak, P. Burchardt, J. Rzeźniczak, 2020, Postepy w kardiologii interwencyjnej = Advances in interventional cardiology.

Beneficial effects of empagliflozin on hematocrit levels in a patient with severe anemia

B. Musielak, P. Burchardt, J. Rzeźniczak, 2021, Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences.

Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers

P. Budzianowski, P. Burchardt, J. Rzeźniczak, 2019, Disease markers.

Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans

D. Dey, D. Berman, P. Slomka, 2022, European Journal of Nuclear Medicine and Molecular Imaging.

Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes

R. Słowiński, P. Budzianowski, P. Burchardt, 2018, Journal of Translational Medicine.

Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method

D. Dey, S. Achenbach, M. Dweck, 2022, Medical Imaging.

Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images

D. Dey, H. Gransar, P. Slomka, 2022, Circulation. Cardiovascular imaging.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

D. Dey, H. Gransar, P. Slomka, 2022, JACC. Cardiovascular imaging.

Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging

P. Slomka, A. Sinusas, Yi-Hwa Liu, 2022, Journal of Nuclear Cardiology.

Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans

D. Dey, D. Berman, P. Slomka, 2022, European Journal of Nuclear Medicine and Molecular Imaging.

Patients with Non-Obstructive Coronary Artery Disease Require Strict Control of All Cardiovascular Risk Factors: Results from the Polish Local Population Medical Records

B. Musielak, P. Burchardt, J. Rzeźniczak, 2021, Journal of clinical medicine.

Mechanical thrombectomy for acute ischemic stroke after implantation of CoreValve Evolut R in a degenerative bioprosthetic surgical valve.

P. Burchardt, J. Hiczkiewicz, K. Pieszko, 2020, Kardiologia polska.

Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks

D. Dey, T. Bateman, E. Miller, 2022, Medical Imaging.

Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms

P. Burchardt, J. Rzeźniczak, J. Hiczkiewicz, 2018, Heart and Vessels.

Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation

S. Fichtlscherer, A. Zeiher, S. Dimmeler, 2020, ESC heart failure.

The Role of Hematological Indices in Patients with Acute Coronary Syndrome

P. Burchardt, J. Rzeźniczak, J. Hiczkiewicz, 2017, Disease markers.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

D. Dey, S. Achenbach, H. Gransar, 2022, The Lancet. Digital health.

Clinical applications of artificial intelligence in cardiology on the verge of the decade

B. Musielak, P. Burchardt, J. Rzeźniczak, 2020, Cardiology journal.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

D. Dey, P. Kaufmann, P. Slomka, 2023, European Journal of Nuclear Medicine and Molecular Imaging.

Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification

D. Dey, A. Lin, K. Pieszko, 2023, BJR open.

Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes

R. Słowiński, P. Burchardt, J. Rzeźniczak, 2018, Journal of Translational Medicine.

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

D. Dey, P. Kaufmann, P. Slomka, 2022, The Journal of Nuclear Medicine.

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

M. Motwani, D. Dey, P. Kaufmann, 2023, NPJ digital medicine.

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events

D. Dey, D. Berman, P. Slomka, 2022, The Journal of Nuclear Medicine.

Deep Learning From Coronary Computed Tomography Angiography for Atherosclerotic Plaque and Stenosis Quantification and Cardiac Risk Prediction

D. Dey, D. Berman, S. Achenbach, 2021, SSRN Electronic Journal.

A Randomized Study Comparing Closed-Incision Negative-Pressure Wound Therapy with Standard Care in Immediate Breast Reconstruction

D. Murawa, M. Wichtowski, J. Boyd, 2022, Plastic and Reconstructive Surgery.

Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography

D. Dey, P. Slomka, M. Dweck, 2023, Medical Imaging.

Cognitive Impairment in Cardiovascular Patients after Myocardial Infarction: Prospective Clinical Study

P. Burchardt, A. Tykarski, J. Rzeźniczak, 2023, Journal of clinical medicine.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging.

P. Slomka, A. Sinusas, A. Feher, 2023, The international journal of cardiovascular imaging.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

P. Slomka, A. Sinusas, A. Feher, 2023, European journal of nuclear medicine and molecular imaging.

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

D. Dey, P. Slomka, A. Sinusas, 2024, EBioMedicine.

Machine learning for prediction of all-cause mortality after transcatheter aortic valve implantation

D. Dey, M. Dweck, L. Nombela‐Franco, 2023, European Heart Journal - Quality of Care and Clinical Outcomes.

The Comparative Method Based on Coronary Computed Tomography Angiography for Assessing the Hemodynamic Significance of Coronary Artery Stenosis

J. Hiczkiewicz, K. Pieszko, R. Zimoląg, 2023, Cardiovascular Engineering and Technology.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

D. Dey, T. Ruddy, P. Kavanagh, 2024, Nature communications.