| Topic title |
Possible scientific supervisors |
Source of funding |
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Innovations in ICP Wave Monitoring for Personalized Neurological Care
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vyresn. m. d. dr. Yasin Hamarat |
state-funded |
Research Topic Summary.
Traumatic Brain Injury (TBI) affects 69 million people annually, with new cases ranging from 100 to 330 per 100,000 each year. TBI is a major cause of death and disability, incurring significant healthcare and societal costs in working age group. Intracranial pressure waveforms hold crucial information about brain health, with potential applications beyond TBI. This proposal explores utilizing these waveforms to enable individualized, precise medical treatments. Multimodal monitoring of intracranial waves can enhance patient outcomes, promptly identify ischemic and hyperemic events, and minimize secondary neurological damage.
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Automated Detection and Classification of Defects in X-ray Computed Tomography Using Machine Learning
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prof. dr. Elena Jasiūnienė |
state-funded |
Research Topic Summary.
X-ray computed tomography (CT) is becoming increasingly popular for visualising of the 3D internal structure of objects in various industries. In many cases visualisation of the structures is not sufficient, and automated detection and classification are also required. The scientific problem addresses the challenges and uncertainties associated with automated defect detection and classification from 3D CT data. Influencing factors such as material properties, beam hardening, filtration, tube current, number of projections, object placement, etc. must to be evaluated. In addition, the effectiveness of different machine learning algorithms in identifying defects will be evaluated.
The objective of the research is to develop automated defect detection and classification algorithm in X-ray computed tomography data using machine learning.
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Assessing the Reliability of Digital Biomarkers Derived from Wearable Health Technologies
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prof. dr. Vaidotas Marozas |
state-funded |
Research Topic Summary.
Relevance. The information technology revolution is transforming people's lives and wearable health technologies are already widely used to monitor cardiovascular and neurological diseases. Artificial Intelligence and Machine Learning (AI/ML) techniques are being applied to the diagnosis of cardiac arrhythmias, sleep disorders, depression, epilepsy, as well as glucose and blood pressure monitoring, allowing personalised treatment.
Problem. Health monitoring outside the clinic faces challenges such as uncertain recording conditions and motion artefacts that make AI/ML models unreliable. Methodologies are needed that allow not only to record biomarkers but also to estimate their uncertainty, increasing the diagnostic reliability of wearable technologies.
Aim - to develop and investigate methods to ensure the reliability of wearable health technologies.
Expected results. The developed methodologies and algorithms will allow to estimate the uncertainty of digital biomarkers, increasing the confidence in wearable technologies, and will be applied to the monitoring of cardiac arrhythmias, sleep disorders and blood pressure.
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| Problems and development of non-invasive technologies for physiological monitoring of human brain and for neuroprotection of brain functions. |
prof. dr. Arminas Ragauskas |
state-funded |
| Application of Guided Lamb Waves for Characterization and Non-Destructive Testing of High Density Polyethylene (HDPE) Pipe Joint Systems |
m. d. dr. Justina Šeštokė |
state-funded |
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Remote metrology methods for monitoring the characteristics of measuring instruments
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doc. dr. Paulius Kaškonas |
state-funded |
Research Topic Summary.
The understanding of object characteristics, processes, phenomena, and other information collection is grounded in quantitative assessment and measurements, with the volume of such data rapidly increasing. Industrial automation and the development of advanced manufacturing methods drive a significant growth in the number of measuring instruments, making remote metrology increasingly relevant. This field focuses on reducing, optimizing, and automating the traditional, contact-based maintenance procedures for measuring devices, aiming to promote more efficient and less intervention-dependent management of maintenance processes. The importance of remote metrology is expanding across the fields of manufacturing, healthcare, energy, and environmental protection, as it enables real-time monitoring and analysis of metrological characteristics of devices, minimizing downtime due to metrological procedures (e.g., calibration in accredited laboratories or verification).
Research and development efforts at the Kaunas University of Technology’s Faculty of Electrical and Electronics Engineering, specifically within the Institute of Metrology and the Department of Electronics Engineering, highlight the growing need for remote metrological monitoring procedures in industry. Advancing this field would involve developing new, legal and industrial metrology accepted methods to replace or supplement traditional and contact-based measurement procedures for assessment and monitoring of the metrological characteristics of measuring instruments 'in situ'.
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Application of artificial intelligence methods for analysis of informative regions and measurement of their quantitative parameters within ultrasonic NDT and medical diagnostic images
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prof. dr. Renaldas Raišutis |
state-funded |
Research Topic Summary.
The scientific problem includes the informative analysis of ultrasound signals and diagnostic images and the quantitative interpretation of the results in order to detect internal defects and pathologies. The aim is to develop and investigate the methods of analysis of informative areas in ultrasound non-destructive testing and medical diagnostic images, providing opportunities for quantitative parameter measurement and automated classification of these areas (internal defects and various pathologies), using artificial intelligence methods. The research will use advanced research infrastructure with global relevance.
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