The Clinical Center of Montenegro recently purchased a microperimeter MAIA (Macular Integrity Assessment) device from CenterVue. This device was purchased for the implementation of the clinical study RECOGNISED.
This diagnostic modality allows analysis of the structural and functional elements of retina by measuring sensitivity mapping and fixation analysis with fundus image in one exam. It is used to diagnose and follow the progression of pathological processes affecting the macular area.
The MAIA device has
auto focus, it is quick, and simple to use, as it does not require
pupil dilatation. Each eye can be examined in less than 3 minutes.
Most importantly, recent studies showed that in people with type 2 diabetes, retinal sensitivity assessed by microperimetry directly correlates with neurodegeneration of the brain. Therefore, this diagnostic method and could be used as a biomarker for detection of patients at risk for developing dementia.
Dr. Isidora Rovčanin Dragović successfully presented her PhD thesis research proposal in front of the following Committee:
Miodrag Radunović, MD, PhD, Dean of the Faculty of Medicine, University of Montenegro
Nataša Popović, MD, PhD, Mentor, Associate Professor at the Department of Medical Physiology, Faculty of Medicine, University of Montenegro
Milica Martinović, MD, PhD, Co-mentor, Full Professor at the Department of Pathophysiology, Faculty of Medicine, University of Montenegro
Elka Stefanova, MD, PhD, Full Professor of Neurology, Faculty of Medicine, University of Belgrade, Serbia
Appolonia Tullo, PhD, Biomedical researcher at the Institute of Biomembranes and Bioenergetics, Bari, Italy
The title of the proposed research was: „ A New Method for Stratification of the Risk for Alzheimer‘s Disease in Patients in Montenegro“. This research is conducted as a part of the project DEMONSTRATE.
The PhD candidate
demonstrated the competency on the subject, the potential for
independent research, and passed the preliminary examination with an
Project DEMONSTRATE team members from Montenegro organized an online conference on November 6th, 2020 in order to organize available facilities and resources, and plan activities for the 2nd year of the project in accordance with the changes related to COVID-19 pandemic.
DEMONSTRATE team members presented their research at the conference 14th World Congress on Controversies in Neurology – CONy 2020, held from October 29th to November 1st 2020. The poster titled “Improving the Diagnosis of Cognitive Impairment in Montenegro- on the Path of Learning” by Isidora Rovčanin Dragović, Ljiljana Radulović, Jevto Eraković, Miodrag Radunović, Goran Popivoda, Tijana Vuković and Nataša Popović was presented at the conference.
Due to on-going
developments with COVID-19 pandemic, this year the conference was
organized completely online.
The Ministry of Science of Montenegro accepted the 12-month progress and financial report for the project DEMONSTRATE. The overall score of the report is 96.5 out of 100 points. The second year of the project and continuation of the financial support has been approved.
Taking into account the new circumstances that arose as a result of the Covid-19 pandemic, the Ministry approved the extension of the project for six months so the planned activities could be adequately implemented. The new contractual deadline for the realization of the project is September 30, 2021.
DEMONSTRATE project was presented in local newspaper Dnevne Novine today. Prof. Nataša Popović gave an interview discussing the project results and new Horizon project called RECOGNISED that started this year (link).
Project DEMONSTRATE was featured in the paper “Lacunarity Analysis of Microvascular Morphology in Human Retina” by I. Konatar, N. Popovic, T. Popovic, M. Radunovic, and B. Vukcevic was presented at the 2020 IcETRAN conference today. More about the conference at the following link.
ABSTRACT – Fractal analysis provides means for the quantitative assessment of geometric patterns in one, two, and three dimensions. It is aimed at analysis of graphical shapes that belong to a class of fractal objects that are characterized by the self-similarity over different scales. Various structures in nature are fractals and fractal analysis techniques are widely used for analysis of biomedical images. One such example of application is analyzing blood vessel structure in the human retina that can be extracted from digital images captured by fundus camera. The most commonly used fractal analysis is estimation of fractal dimension using various box-counting methods for mono-and multi-fractals. Although two fractal images can have the same fractal dimension they can have very different appearance and structure. One can appear as a structure that fills most of the available space, while the other can have a lot of empty areas. These differences can be quantified by lacunarity parameter, which has greater value in images with less space-filling properties. This paper focuses on the estimation of the lacunarity parameter implemented in the Python programming language, which is aimed at lacunarity analysis of microvaculae morphology in human retina. The implementation is validated by comparison with the results obtained by ImageJ, a commonly used software for analysis of biomedical images. The value of the lacunarity analysis is demonstrated on a set of actual images of human retina associated with different medical conditions.
The paper “Retinal Microperimetry: A New Tool for Identifying Patients With Type 2 Diabetes at Risk for Developing Alzheimer Disease published by Ciudin et al in Diabetes in 2017 represents an important step forward in the development of non-invasive detection of patients at risk for the development of Alzheimer disease.
This is especially important in patients with type 2 diabetes, who have two-fold higher risk for developing dementia than age-matched people without diabetes, even after taking into account vascular risk factors. The paper demonstrated that retinal sensitivity assessed by microperimetry directly correlates with signs of brain neurodegeneration detected by MRI and FDG-PET. More importantly, retinal sensitivity significantly correlates with cognitive status of the patients.
These data suggest
that retinal microperimetry could be applied as a new tool in
identification of patients at risk for Alzheimer disease.