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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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Przemysław Korzeniowski


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.

Sano Centre for Computational Medicine

Czarnowiejska 36, 33-332, Cracow, Poland

Email: ">

Tel: +48 12 307 27 37

Team Members

Jan Kwapisz


Bio: Obtained doctoral degree in 2022 with the thesis titled “Beyond the Standard Model of particle physics and cosmological standard model: Quantum Gravity Perspective”. Received a couple of scientific grants and awards such as the Ministry Scholarship for outstanding scientists and the Etiuda grant. Gave lectures in such places as MIT, NUS, Cambridge and Max Planck Institute. Currently working in health informatics group on AI and medical simulations. Also academic lecturer at Faculty of Physics, University of Warsaw.

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.

Joanna Kaleta

PhD Student

She obtained the title of MSc. in Computer Science at the Warsaw University of Technology specializing in machine learning. In her research, she pays special attention to projects with valuable ideas that contribute to real positive changes in the world. Previously she worked on topics related to medical signal analysis, biodiveristy and smart cities. As a PhD student at Sano in Health Informatics team she will focus on fetal image analysis and image-guided therapy using deep learning methods.

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.

Natalia Lipp

PostDoc in Health Informatics Team

Natalia is Ph.D. Student in social sciences. Her doctoral research concerns the psychological factors of human-computer interaction, particularly the role of immersion and imagination in task performance in virtual reality. At Sano, Natalia is devoted to enhancing patients' well-being and increasing trust to AI. In addition to scientific work, Natalia is also an academic lecturer at Jagiellonian University, and she teaches Health Psychology, Statistics, and Social Psychology.

Sabina Kamińska

PhD Student

She is a technology-loving software developer. She has nearly 2-years of experience in VR and mobile games. She has been passionate about biomedical engineering. She graduated from the Silesian University of Technology with a Master's Degree in Biomedical Engineering specialising in Informatics in Medicine.The purpose of the engineering and master's thesis was to improve the hand rehabilitation system. For the purpose of the project she designed game rehabilitation and glove construction. At Sano, she joined the Healthcare Informatics team and she’ll be working on deep Learning-based Surgical Robots.

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.

Kuba Chrobociński

PhD Student

Kuba graduated from AGH University of Science and Technology with a Master's degree in Biomedical Engineering. His main areas of professional interest are processing of medical imaging, machine learning and AI in medicine and joining those two in VR environment. Kuba is passionate about using technology to help people live longer, healthier lives. He joined Sano in October 2022 as PhD student. His PhD project will be conducted in collaboration with University of Sheffield on topic of Haptically-enhanced VR/AR for Improved Information Capture and Exchange in Medical/Social Environments. Privatly he is interested in trekking, wine tasting, cooking and Formula 1.

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


Plotka S Korzeniowski P, Brawura-Biskupski-Samaha R

Virtual Reality Simulator for Fetoscopic Spina Bifida Repair Surgery Conference


Abstract | BibTeX | Links:

Sniezynski B Pajor A, Zolnierek J

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



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:

Szymon & Wlodarczyk Płotka, Tomasz & Klasa

FetalNet: Multi-Task Deep Learning Framework for Fetal Ultrasound Biometric Measurements Workshop


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:

Moshe; Levanony Raboh, Dana; Dufort

Context in medical imaging: the case of focal liver lesion classification Bachelor Thesis


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:

Graf B Xie Y, Farzam P

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


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.


Sitek A Szczepański T, Trzciński T

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


BibTeX | Links:

Satława T Grzeszczyk MK, Lungu A

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


BibTeX | Links:

Michal K. Grzeszczyk Magdalena Dul, Ewelina Nojszewska

Estimation of the Impact of COVID-19 Pandemic Lockdowns on Breast Cancer Deaths and Costs in Poland using Markovian Monte Carlo Simulation Conference

International Conference on Computational Science, 0000.


Open Positions