Research

Main Research Directions:

• Predictive modeling and disease assessment in modern medicine:
  This research area focuses on retrospective and prospective analysis of diseases to understand their occurrence, progression, and clinical impact. By leveraging modern technologies, including artificial intelligence, machine learning, and medical data analysis, predictive models for early diagnosis and risk stratification of patients will be developed and validated. The aim is to support personalized medicine, improve treatment outcomes, and optimize healthcare for geriatric and other at-risk groups.
• Analysis of movement patterns using video technologies and artificial intelligence:
  The goal of this research is the objective assessment of patients’ motor functions and movement patterns through advanced video analysis. By applying computer vision and machine learning methods, a system will be developed for the automatic detection, classification, and quantification of movement abnormalities in various patient groups. This approach will enable early detection of functional disorders, monitoring of disease progression, and assessment of rehabilitation effectiveness, thereby supporting a personalized approach in modern medicine.
• Use of virtual reality in clinical practice:
  The research focuses on the implementation of virtual reality (VR) technologies into the daily operation of healthcare facilities. The goal is to assess the benefits of VR in simplifying routine tasks, such as interactive completion of medical history questionnaires, while also developing innovative approaches to rehabilitation. The research team will study the effectiveness, patient and healthcare personnel acceptance, and the impact of these technologies on care quality and clinical outcomes.
• Monitoring of physical activity using wearable technologies:
  This research direction focuses on utilizing data from wearable devices for continuous assessment of patients’ physical activity. The aim is to identify deviations from typical movement patterns that may signal the onset of disease, deterioration of health status, or postoperative complications. The research will aim to develop predictive and monitoring tools that support early intervention, personalized care, and long-term patient monitoring in home settings.
• Use of 3D printing in preoperative visualization and planning of therapeutic interventions:
  This research area focuses on the use of spatial visualization through 3D printing to improve the understanding of anatomical relationships and specific surgical, radiotherapeutic, or interventional clinical situations. Products from the silicone laboratory will also enable implementation in radiotherapy practice.

Research Objectives:

The main goals include the development of predictive models (e.g., for sarcopenia), movement analysis using video and AI systems, integration of virtual reality and 3D printing into clinical processes, and activity tracking via wearables. These approaches will enable early disease detection, more effective treatment, and individualized care. The benefits will include higher quality healthcare, improved therapeutic outcomes, and greater patient engagement.

Updated: 30. 07. 2025