Ajčević Miloš

Milos Ajcevic serves as Assistant Professor in Biomedical Engineering at the Department of Engineering and Architecture, University of Trieste, specializing in biomedical signal and image processing with emphasis on neurophysiological signals and neuroimaging. His work bridges engineering and clinical neuroscience to address neurological disorders and chronic disease management.

Education:

• B.Sc. in Electronic Engineering, University of Trieste
• M.Sc. in Electronic Engineering (summa cum laude, 2012), University of Trieste
• Ph.D. in Information Engineering (Biomedical Engineering curriculum, 2015), University of Trieste

Dr. Ajcevic's research integrates advanced signal processing with machine learning to analyze neurophysiological data (EEG, PET, CT) for conditions including Alzheimer's, Parkinson's, and post-COVID cognitive impairment. His methodology emphasizes non-invasive diagnostic techniques and computational modeling for neurological assessment, with growing applications in hepatology and wound management through AI-driven solutions.

Analysis of his 2025 publications reveals dominant trends in leveraging large language models for clinical decision support (particularly in hepatology via EASL guidelines), multimodal neuroimaging for post-COVID neurological sequelae, and deep learning for medical image segmentation. Key thematic clusters include AI safety validation in gastroenterology, non-invasive portal hypertension diagnosis using pan-elastographic models, and EEG-based biomarkers for stroke and neurodegenerative disorders.

Dr. Ajcevic has secured research funding through multiple competitive projects including PARIDE and ECRES-DiabEx (2015-2016) for chronic disease self-management systems, followed by Airalzh, MeMoRi-NET, and CASSIA (2016-2022) focused on neurological disorders. He currently contributes to the Twin Brain H2020 project developing mobile brain/body imaging solutions. As an active member of EURASIP, IFMBE, and Italy's GNB group, he participates in international collaborative networks advancing biomedical signal processing standards.