Finansijski program : Ministarstvo nauke CG
Naziv [ENG] : Artificial Intelligence Supported of Novel Non-Invasive Biomarkters of Aging
Naziv : Vještačka Inteligencija u Identifikaciji Novih Neinvazivnih Biomarkera Starenja
Početak : 03.01.2024.
Kraj : 03.01.2027.
Skraceni naziv : AI-AGE
Web site : https://ai-age.udg.edu.me/
Tip projekta : naučno-istraživacki
Tematska oblast : 2. IKT, 4. Medicina i zdravlje ljudi;
Jedinica : Medicinski fakultet
Budzet za jedinicu :
Ukupan budzet : 144430
Rukovodilac : Popović Nataša

Opis : The overall objective of the AI-AGE project is to strengthen research excellence by building scientific and innovation capacity in the field of artificial intelligence (AI) and high-perfromance computing (HPC) for application in heath and aging. This is an interdisciplinary research two priority areas of the Smart Specialization Strategy (2019-2024) for Montenegro: ICT and Medicine and human health. The project leverages on the results and experiences fro previous projects (DEMONSTRATE, EURCC, RETINAL, RECOGNISED) ad Collaborations of partners, which already resulted in joint publications in recognized scientific journals. Experienced researchers from the University of Donja Gorica an The University of Montenegro have already established interdisciplinary research and collaboration with renowned international teams including Montenegrin scientific diaspora, and the pain purpose of this proposal is to support engagement of young researchers while encouraging gender equality. The eye serves as a window for non/invasive assessment of retinal vascular and neural tissue, offering valuable insight into our health Extensive research has established the association between changes in retinal morphology and the increase risk of many age-related chronic diseases. These changes are also linked to healthy aging,albeit more pronounced in the presence of age-related chronic conditions. The AI-AGE project proposes the use of machine learning (ML) algorithms and evaluation of state-of-the-art AI tools to train and create prediction models to identify novel non-invasive biomarkers of aging, and increase risk for development of age-relate conditions The idea is to utilize a large dataset of annotated retinal images from the UK BioBank, to explore deep learning (DL) techniques, most commonly based n convolutional neural networks (CNNs), such as U-Net, and Res-Net, and transformers, but also to expand the research n the use of ensamble methods that combine ML techniques to improve performance and accuracy. Finally, the research in the AI/ML domain would include experimentation and integration with Large Language Models (LLMs) such as ChatGPT and Brad, that could find use in advanced annotation, interpretation and communication f results, all in the function of crafting novel decision support in diagnostics.