Institute of Fundamental Technological Research
Polish Academy of Sciences


Zofia Rudnicka, MSc

Department of Information and Computational Science (ZIiNO)
Research Group for Neuroinformatics (ZeBNI)
position: PhD Student
PhD student
telephone: (+48) 22 826 12 81 ext.: 289
room: 413

Recent publications
1.  Rudnicka Z., Szczepański J., Pręgowska A., Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview, Electronics , ISSN: 2079-9292, DOI: 10.3390/electronics13040746, Vol.13, No.4, pp.1-35, 2024

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

artificial intelligence, extended reality, medical image scan segmentation

Rudnicka Z. - IPPT PAN
Szczepański J. - IPPT PAN
Pręgowska A. - IPPT PAN
2.  Rudnicka Z., Pręgowska A., Glądys K., Perkins M., Proniewska K., Advancements in artificial intelligence-driven techniques for interventional cardiology, Cardiology Journal, ISSN: 1897-5593, DOI: 10.5603/cj.98650, pp.1-31, 2024

This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

artificial intelligence (AI), interventional cardiology (IC), cardiac modalities, augmented and/or virtual realities, automatic medical systems

Rudnicka Z. - IPPT PAN
Pręgowska A. - IPPT PAN
Glądys K. - other affiliation
Perkins M. - other affiliation
Proniewska K. - Jagiellonian University (PL)

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