Pracownicy

Streszczenie:
Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by synovial immunopathology, where innate immune activity and neurobiological remodeling necessitate timely and precise diagnostic interventions. While
thermography provides a non-invasive window into the altered perfusion and thermal dynamics associated with such joint inflammation, its clinical adoption has been hindered by the computational demands of traditional
AI. We address this by proposing a novel Spiking Neural Network (SNN) framework that aligns diagnostic automation with the event-driven nature of physiological signals. By encoding spatial temperature patterns into
temporally structured spike trains, our approach introduces a biologically inspired static-to-dynamic translation, where temporal structure is computationally derived from spatial thermal distributions rather than directly measured inter-frame dynamics. To ensure statistical rigor, a strict patient-level data split was applied to a dataset of
291 healthy controls and 186 RA patients. We evaluated three SNN paradigms:Tempotron, Surrogate Gradient Learning (SGL), and Bio-Inspired Active Learning (BAL) to optimize the trade-off between diagnostic precision and efficiency. The Tempotron learning rule achieved a peak validation accuracy of up to 90.62% on a fixed patient-level split, demonstrating superior sensitivity to spatio-temporal signatures, while SGL offered the most efficient training convergence (563 s). Notably, our framework exhibits strong potential for reduced energy demands compared to traditional frame-based architectures. As one of the first studies to explore the intersection of neuromorphic computing and thermographic signatures associated with synovial inflammation, this study demonstrates the potential of spiking neural networks as lightweight and biologically inspired tools for automated RA screening in resource-constrained settings.

Słowa kluczowe:
Spiking neural networks (SNN), Rheumatoid arthritis (RA), Thermographic imaging, Bio-inspired learning algorithms, Green AI, Neuromorphic computing

Słowa kluczowe:
Spiking neural networks, Neuron models, Learning algorithms, Lempel-Ziv complexity

Streszczenie:
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

Słowa kluczowe:
Artificial Intelligence,Machine Learning,Metaverse,Virtual Reality,Extended Reality,Augmented Reality,Digital Twin,Health Digital Twin,personalized medicine,cardiology

Streszczenie:
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.

Słowa kluczowe:
artificial intelligence, extended reality, medical image scan segmentation

Streszczenie:
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.

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

Streszczenie:
This study compares three SNN training methods on MNIST: ANN-to-SNN [1], surrogate gradient backpropagation [2], and Tempotron learning [3]. All were trained under identical conditions. Key hyperparameters were optimized using Optuna, and the best configurations were used for retraining.

This two-stage approach improved accuracy and stability. ANN-to-SNN offered solid baseline performance, BP_Surspike achieved higher accuracy with greater cost, and BP_Tempotron balanced biological realism and efficiency.

These results confirm that learning method choice strongly affects SNN performance. Hyperparameter optimization, supported by Optuna, was key to achieving accuracy and training stability. The study also shows how Tempotron, surrogate gradient backpropagation, and ANN-to-SNN can complement each other in hybrid, biologically plausible SNN frameworks [4].

The author gratefully acknowledges the support of the Institute of Fundamental Technological Research, Polish Academy of Sciences.

Słowa kluczowe:
spiking neural networks, hyperparameter optimization

Streszczenie:
As digital technologies become integral to modern education, cybersecurity has become a critical concern for educators, institutions, and policymakers. Schools are increasingly targeted by cyber threats such as phishing, ransomware, data breaches, and cyberbullying. These
incidents disrupt educational continuity and compromise the safety, privacy, and trust of students and staff. This paper explores the evolving cybersecurity landscape in educational environments, identifying systemic vulnerabilities and barriers to implementation—such as limited resources, lack of teacher training, and technological disparities. It highlights current strategies for strengthening cybersecurity, including secure learning management systems, two-factor
authentication, cybersecurity awareness training, and the integration of digital hygiene practices into curricula. A key contribution of this work is its focus on emerging cybersecurity concerns within artificial
intelligence applications, particularly neural networks used in educational tools. We examine vulnerabilities such as adversarial attacks, data poisoning, and model inversion, which threaten the integrity of Artificial Intelligence (AI) -driven learning systems. These issues are addressed through ongoing interdisciplinary research by our team, which aims to identify and mitigate risks at the intersection of education, data science, and neural computation. By combining policy analysis, educational best practices, and technical insights from AI security research, this study proposes a comprehensive, forward-thinking approach to cybersecurity. It advocates for a digital learning ecosystem that is secure, inclusive, and resilient against both current and future cyber threats.