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Health Informatics

Manages the use of patient healthcare information and deals with the resources, devices, and methods required to optimize acquisition, storage, retrieval, and use of information in medicine. This team concentrates on a new generation of approaches to medical communication and incorporation of output of computational methods (data science, in-silico methods) in medical workflows. New models of information exchange between existing decision agents (patients, their families, doctors, care teams) are considered and investigated. Intuitive interfaces for improved understanding of health-related data and AI insights will be an important part of this effort.

This team will, among others, investigate new computer interfaces for enhanced perception of multi-modality, multi-source medical data, including 3D virtual interactive environments. Using such environments for advanced remote interactive communication without the need for physical co-presence will improve today’s telemedicine, also in the context of pandemic threats. For smaller medical centres, and rural area care, these solutions will allow for remote virtual interaction between patients and specialists, and advanced, data-driven specialist consultations. The Health Informatics team will also investigate optimal decision-making when heterogeneous information sources – machine recommendations, expert human knowledge, and patient preferences – are combined during the health-related decision-making process.

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Arkadiusz Sitek

Director of Sano, President of the Management Board

Knowledge of the physics of medical imaging including radiography, CT, US, MRI, and nuclear imaging techniques (PET and SPECT), and applications of these imaging technologies in hospitals and clinical trials in the fields of neurology, oncology, and cardiology. Experience in applications of healthcare informatics in clinical settings.

Expertise with extensive publication record in medical image acquisition, inverse problems, tomographic image reconstruction, image analysis/modeling, image artifacts.

Extensive knowledge and experience in the application of machine learning (ML) models to several types of data ranging from data in computer vision, text processing, and feature based machine learning. Large scale applications of ML in the industry.

Expert in applied statistics (classical and Bayesian).

Author of more than 65 peer-reviewed publications in top medical imaging, clinical, engineering, and statistics journals and more than a hundred lectures, invited talks, conference talks, conference abstracts, conference proceedings, book chapters, books, and editorials. The author of the book “Computational statistics in nuclear imaging” published in 2014 by CRC Press/Taylor and Francis.

Inventor of 20 patents (granted, published, and filed) in medical image processing, clinical informatics, tomographic data reconstruction, and artificial intelligence in healthcare. Successful Innovator Award from the Technology and Ventures Office, Beth Israel Deaconess Medical Center award in 2012. Contributed to several technological solutions currently used in clinics around the world.

Presenter of invited lectures at the Harvard Medical School, John Hopkins Medical Center, Perelman School of Medicine, University of Pennsylvania, IBM and many others.

Member of advisory board (2017-) and named in the top 25 referees in 2013 and 2014 for the Physics in Medicine and Biology. Recognized by the Editor of Radiology in 2013, 2014, 2016, with special distinction in 2017-2020. Chaired scientific sessions at RSNA, IEEE Medical Imaging Conference, Society of Nuclear Medicine, Fully 3D image reconstruction meetings. Assistant to IEEE Medical Imaging Conference chair 2015 and 2017. Referee for many journals and conferences.

  • 2018 – 2020 Senior Data Scientist, IBM Watson Health, Cambridge, MA
  • 2015 – 2018 Senior Scientist, Philips Research North America, Cambridge, MA
  • 2006 – 2015 Assistant Professor of Radiology, Harvard Medical School, Boston, MA
  • 2012 – 2015 Associate Physicist, Massachusetts General Hospital, Boston, MA
  • 2006 – 2012 Research Associate Brigham and Women's Hospital, Boston, MA
  • 2004 – 2006 Staff Scientist, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA
  • 2001 – 2004 Imaging Scientist, Beth Israel Deaconess Medical Center, Boston, MA
  • 2001 – 2004 Instructor in Radiology, Harvard Medical School, Boston, MA
  • 2000 – 2001 Visiting Scientist, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA
  • 1998 – 2001 Postdoc/Research Associate, University of Utah, Salt Lake City, UT
  • 1998 Ph.D in Physics, University of British Columbia, Canada
  • 1994 M.Sc in Physics, University of Warsaw, Poland

Sano Centre for Computational Medicine

Czarnowiejska 36, 33-332, Cracow, Poland


Tel: +48 12 307 27 37

Team Members

Przemysław Korzeniowski

Senior Postdoctoral Researcher, Head of VR & Robotics

He obtained M.Sc. in Advanced Computing in 2010 and Ph.D. in Modelling and Simulation in 2016 from Imperial College London. His work and research at Modelling in Medicine and Surgery Research Group focused on the development and validation of virtual reality simulators. He gained practical experience in the industry at the R&D Department of Volkswagen Group, where he was a key team-member of a newly established Virtual Engineering Lab, the forefront of digital transformation of the whole company. His main research interest are virtual and augmented reality, real-time physically-based simulation, massively-parallel computing, haptic interfaces as well as aspects of software engineering and architecture of simulation software.

Paweł Renc

PhD Student

Paweł obtained the title of MSc. in CS at the AGH University of Science and Technology in Krakow, specializing in data science, and currently is a PhD student at the same university. At Sano, he joined the Healthcare Informatics team where he processes medical data utilizing machine learning methods. His research interests include AI, optimization algorithms and parallel computing in the GPGPU architecture.

Michał Grzeszczyk

PhD Student

Michał is PhD student who joins Health Informatics team and will work on Machine learning estimation of pulmonary circulation abnormalities using phase contrast MR and echocardiography. He is a graduate of Computer Science studies at the Warsaw University of Technology and Technical University of Berlin (dual-degree). At Sano he'll be working on non-invasive pulmonary hypertension detection. He is passionate about utilizing of AI in various areas. After hours, he works with his friend on the mobile application Chefs' ( which is devoted to storing and sharing cooking recipes coming from multiple sources like images or websites. In his free time he loves playing football and discovering new sport disciplines.

Amanuel Ergogo


Amanuel is a Ph.D. student working on a project entitled Machine Learning for Collaborative Robots in Healthcare. In the Sano health informatics team. His main research interest is in computer vision and deep learning for motion planning, controls, and robot autonomy, specifically to provide robotic assistance and manipulation aids in hospital settings. He obtained M.Eng in Control Engineering and Robotics from Wroclaw Science and Technology University in Poland. His Master's thesis deals with Autonomous mobile robot localization problems, that is to enable the robots to localize themselves in harsh environmental conditions. He earned his BSc degree in Electrical and Computer Engineering from Wolaita Sodo University in Ethiopia. He has experience working as an assistant lecturer at the same university where he earned his bachelor’s degree.

Arkadiusz Pajor

MSc Student

Arkadiusz holds BE in Information and Communication Technology (ICT) from AGH University of Science and Technology and currently is pursuing MSc degree in Data Science there. He was leading scientific club "Telephoners" for two years. On the on hand he is passionate of Software Engineering, good practices, design patterns and modern software architecture. On the other hand he is a big fan of automating things using Artificial Intelligence. Currently he is doing reasarch on Interpretable Machine Learning (IML) models in order to build medical decision support system. His free time he spend on traveling, reading fantasy and sci-fi books, playing games and taking part in hackathons.

Szymon Płotka

PhD Student

He obtained M.Sc. in Medical Informatics in 2019 from Warsaw University of Technology. Currently pursuing his PhD in the medical image analysis at Sano Centre and Warsaw University of Technology. His main research interests are computer vision, machine learning and deep learning-based fetal ultrasound imaging and image-guided therapy.

Tomasz Szczepański

PhD Student

Tomasz holds a BEng from Warsaw University of Technology (WUT) in Photonics Engineering. His bachelor thesis focused on computer vision and augmented reality techniques. He gained practical experience as a software engineer at Curious Element startup and The Centre for Innovation and Technology Transfer Management of WUT. He obtained MSc in Computer Science in 2022 from WUT. His thesis focused on the problem of data bias in chest X-rays of patients with COVID-19. Currently, he is pursuing his PhD at Sano Centre and WUT. He joined the Health Informatics team at Sano, and he will be working on medical treatment planning using deep learning methods. In his free time, he bakes Neapolitan pizza and brews craft beer or speciality coffee.

Current Projects

Sano Team: Szymon Płotka, Arkadiusz Sitek

Goal: Preterm Birth Prediction based on fetal transvaginal ultrasound videos using Deep Learning methods


  • University Centre of Mother and Child’s Health, Medical University of Warsaw
  • Michał Lipa, Medical University of Warsaw
  • Tomasz Trzciński, Warsaw University of Technology


  • 200 independent fetal transvaginal US recordings

Preliminary results:

  • Deep learning methods are promising for preterm birth prediction
  • Physical biomarkers could be a good preterm birth indicator
  • Publication in preparation


Bradshaw, Tyler J.; Boellaard, Ronald; Dutta, Joyita; Jha, Abhinav K.; Jacobs, Paul; Li, Quanzheng; Liu, Chi; Sitek, Arkadiusz; Saboury, Babak; Scott, Peter J. H.; Slomka, Piotr J.; Sunderland, John J.; Wahl, Richard L.; Yousefirizi, Fereshteh; Zuehlsdorff, Sven; Rahmim, Arman; Buvat, Irène

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development Journal Article

In: Journal of Nuclear Medicine, 2022.

Abstract | BibTeX | Links:

Grzeszczyk, Tadeusz A.; Grzeszczyk, Michal K.

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models Journal Article

In: Energies 2022, 15(5), 1852;, 2022.

Abstract | BibTeX | Links:

Płotka, Szymon; Klasa, Adam; Lisowska, Aneta; Seliga-Siwecka, Joanna; Lipa, Michał; Trzciński, Tomasz; Sitek, Arkadiusz

Deep learning fetal ultrasound video model match human observers in biometric measurements Journal Article

In: Physics in Medicine & Biology, 2022.

Abstract | BibTeX | Links:

Renc, Paweł; Pęcak, Tomasz; Rango, Alessio De; Spataro, William; Mendicino, Giuseppe; Wąs, Jarosław

Towards efficient GPGPU Cellular Automata model implementation using persistent active cells Journal Article

In: Journal of Computational Science, 2022.

Abstract | BibTeX | Links:

Y, Xie; B, Graf; P, Farzam; B, Baker; C, Lamoureux; A, Sitek

Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT Journal Article

In: SPIE Medical Imaging, 2022.

Abstract | BibTeX | Links:

Otaki, Yuka; Kriekinge, Serge D. Van; Wei, Chih-Chun; Kavanagh, Paul; Singh, Ananya; Parekh, Tejas; Carli, Marcelo Di; Maddahi, Jamshid; Sitek, Arkadiusz; Buckley, Christopher; Berman, Daniel S.; Slomka, Piotr J.

Improved myocardial blood flow estimation with residual activity correction and motion correction in 18 F-flurpiridaz PET myocardial perfusion imaging Journal Article

In: European Journal of Nuclear Medicine and Molecular Imaging , 2021.

Abstract | BibTeX | Links:

Raboh, Moshe; Levanony, Dana; Dufort, Paul; Sitek, Arkadiusz

Context in medical imaging: the case of focal liver lesion classification Journal Article

In: Physics of Medical Imaging, 2021.

Abstract | BibTeX | Links:

Płotka, Szymon; Włodarczyk, Tomasz; Klasa, Adam; Lipa, Michał; Sitek, Arkadiusz; Trzciński, Tomasz

FetalNet: Multi-task deep learning framework for fetal ultrasound biometric measurements Conference

Conference on Neural Information Processing 2021.

BibTeX | Links:

Sitek, Arkadiusz; Ahn, Sangtae; Asma, Evren; Chandler, Adam; Ihsani, Alvin; Prevrhal, Sven; Rahmim, Arman; Saboury, Babak; Thielemans, Kris

Artificial Intelligence in PET: an Industry Perspective Journal Article

In: PET Clinics, 2021.

Abstract | BibTeX | Links:

F, Ślazyk; P, Jabłecki; M, Malawski; P., Płotka

CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated Learning Conference

22nd International Conference on Computational Science 0000.


A, Pajor; B, Sniezynski; J, Zolnierek; A, Sitek

Effect of feature discretization on classification performance of explainable scoring-based machine learning model Conference

22nd International Conference on Computational Science 0000.


T, Szczepański; A, Sitek; T, Trzciński; S, Płotka

POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection Conference

22nd International Conference on Computational Science 0000.


P, Orzechowski; P, Renc; JH, Moore; A, Sitek; J, Was; J, Wagenaar

Are Evolutionary Classifiers Any Good? A Comparative Study on a Synthetic Machine Learning Benchmark. Conference

The Genetic and Evolutionary Computation Conference 2022 0000.


MK, Grzeszczyk; T, Satława; A, Lungu; A, Swift; A, Narracott; R, Hose; T, Trzcinski; A, Sitek

Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models Conference

22nd International Conference on Computational Science 0000.


Open Positions