Projects / Programmes source: ARIS

Machine learning based medical image analysis for prognosis of brain disease course and therapy efficacy

Research activity

Code Science Field Subfield
2.06.10  Engineering sciences and technologies  Systems and cybernetics  Medical informatics 

Code Science Field
2.06  Engineering and Technology  Medical engineering  
image analysis, image processing, machine learning, predictive models, magnetic resonance imaging, neurodegenerative diseases, multiple sclerosis, disease course prediction, therapy efficacy assessment
Evaluation (rules)
source: COBISS
Data for the last 5 years (citations for the last 10 years) on December 5, 2023; A3 for period 2017-2021
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  523  12,144  11,363  21.73 
Scopus  601  15,378  14,330  23.84 
Researchers (31)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  38326  Jernej Avsenik  Neurobiology  Researcher  2020 - 2023  65 
2.  53941  Žiga Bizjak  Systems and cybernetics  Researcher  2020 - 2023  20 
3.  34774  PhD Gregor Brecl Jakob  Neurobiology  Researcher  2020 - 2023  69 
4.  25528  PhD Miran Burmen  Systems and cybernetics  Researcher  2020 - 2023  107 
5.  51911  Lara Dular  Systems and cybernetics  Junior researcher  2020 - 2022  11 
6.  34906  Tine Holc    Technical associate  2020 - 2023 
7.  14075  PhD Alenka Horvat Ledinek  Neurobiology  Researcher  2020 - 2023  137 
8.  33933  Dejan Hribar    Technical associate  2020 - 2023 
9.  54320  Kristijan Ivanušič  Neurobiology  Researcher  2023 
10.  17708  Regina Klavžar    Technical associate  2020 - 2023 
11.  35410  PhD Žiga Lesjak  Systems and cybernetics  Researcher  2022  11 
12.  15678  PhD Boštjan Likar  Systems and cybernetics  Researcher  2020 - 2023  381 
13.  51505  Alja Longo  Neurobiology  Researcher  2020 - 2023 
14.  34298  PhD Samo Mahnič-Kalamiza  Systems and cybernetics  Researcher  2021 - 2022  70 
15.  36457  PhD Peter Naglič  Systems and cybernetics  Researcher  2020 - 2022  48 
16.  20710  Nuška Pečarič Meglič  Neurobiology  Researcher  2020 - 2023  93 
17.  06857  PhD Franjo Pernuš  Systems and cybernetics  Researcher  2020 - 2023  519 
18.  17712  MSc Janez Podobnik  Neurobiology  Researcher  2020 - 2023  61 
19.  28885  PhD Peter Popović  Oncology  Researcher  2020 - 2023  510 
20.  55680  Domen Preložnik  Computer science and informatics  Researcher  2021 - 2023 
21.  15441  PhD Uroš Rot  Neurobiology  Researcher  2020 - 2023  178 
22.  20836  Andrej Sirnik  Medical sciences  Researcher  2020 - 2023  24 
23.  08752  PhD Saša Šega Jazbec  Cardiovascular system  Researcher  2020 - 2023  199 
24.  28465  PhD Žiga Špiclin  Systems and cybernetics  Head  2020 - 2023  135 
25.  33508  PhD Katarina Šurlan Popović  Neurobiology  Researcher  2020 - 2023  258 
26.  20383  PhD Dejan Tomaževič  Manufacturing technologies and systems  Researcher  2021 - 2022  91 
27.  51425  Tina Vipotnik Vesnaver  Cardiovascular system  Researcher  2020 - 2023  68 
28.  57224  Matej Vouk  Medical sciences  Researcher  2023 
29.  28076  PhD Matej Vrabec  Medical sciences  Researcher  2020 - 2023  20 
30.  23404  PhD Tomaž Vrtovec  Systems and cybernetics  Researcher  2020 - 2023  198 
31.  50679  MSc Yevhen Zelinskyi  Systems and cybernetics  Junior researcher  2020 - 2022  12 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0312  University Medical Centre Ljubljana  Ljubljana  5057272000  75,513 
2.  1538  University of Ljubljana, Faculty of Electrical Engineering  Ljubljana  1626965  27,578 
This project will directly address the priority field Research on neurodegenerative diseases (JPND) as set by the Slovenian research agency. Without loss of generality, this project will focus on multiple sclerosis (MS), since it involves two common pathologies also found in cerebrovascular disease, Alzheimer's and other dementias, depression, schizophrenia and bipolar disorder, etc. The two pathologies of interest are scar tissue or lesions and progressive loss of neurons or neurodegeneration, which typically are one of the few paraclinical symptoms that appear from several months up to a few years before any clinical symptoms can even be recognized. Magnetic resonance (MR) tomographic imaging is by far the most sensitive soft tissue imaging technique and therefore extensively used for the assessment of normal brain structure status and the detection of pathologic lesions. Based on brain MR scans the lesion accumulation and degree of neurodegeneration may be quantified in vivo. This is also the reason why information-rich brain MR images are being increasingly utilized in large-scale clinical trials, for instance, for testing new drugs and therapies the MR image based measures are already used as surrogates of clinical outcome measures. In this way, drug development proceeds faster and is thus less hazardous for the enrolled patients. Whereas clinical trials involve between-group comparisons, clinical management of individual MS patients is more challenging and the use of MR image based measures or imaging biomarkers has not yet proliferated. Main difficulties are high inter­- and intra-­observer variability of visual assessment of the MR images and high variability in quality of standard-of-care MR images. The emerging technical solution leading to more objective and reliable quantitative assessment is a computational analysis of MR images. In this project, we will leverage advanced machine learning tools for MR image analysis with the goal to develop accurate, interpretable and robust prediction models that address the need for personalized early prognosis of disease course and treatment efficacy for MS and other neurodegenerative diseases from standard-of-care MR images. The proposed project has 8 deliverables, which involve (1) acquisition and annotation of brain and spinal cord MR images of MS patients and (2) collection of associated set of clinical, laboratory, gait, balance and self-reported outcome measures. For the standard-of-care MR images (3) automated pipeline including MR sequence identification and image quality assessment and (4) adversarial based image enhancement will be developed, followed by (5) feature-rich MR image description based on brain and spinal cord segmentation, radiomics and shape features, and autoencoder representations. Next, (6) based on features and enhanced MR images we will develop novel and improved prediction models, focusing also on interpretability and robustness. Besides (7) expected impactful research publications, the ultimate goal is to (8) develop, integrate and prospectively validate a decision support system for managing MS based on the state-of-the-art prediction models and verify its capabilities using routine, standard-of-care MR images. Preliminary results suggest that MR image based treatment efficacy prediction enables the clinicians to determine the optimal treatment within 1.2 years on average, compared to current average of 3.9 years based on using clinical outcome measures like the extended disability status score (EDSS) or relapse occurrence. Besides, through the search for the optimal prediction models we also aim to elucidate the rather notorious associations between the MR imaging data and clinical outcomes in individual MS patients. The proposed methods and systems have a clear benefit for the patient and clinical MS disease management, but may also help decrease the high socioeconomic costs associated with the MS disease.
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