Viðburðir eftir árum

Ph.D. Thesis Defense - Marco Recenti


  • 8.3.2023, 10:30 - 11:30, Háskólinn í Reykjavík


Candidate: Marco Recenti
Date and Time: March 8, 2023, 10:30 - 11:30, room M208

Thesis Committee:
Supervisor - Paolo Gargiulo, Professor, Reykjavík University, Iceland
Co-Advisor - Magnús Kjartan Gíslason, Associate Professor, Reykjavík University, Iceland
Antonio Fratini, Associate Professor, Aston University, Birmingham, UK
Hannes Petersen, Professor, University of Iceland, Faculty of Medicine, Reykjavik, Iceland

Thesis Examiner:
Prof. Leandro Pecchia, university Campus Bio-Medico Di Roma


Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between different biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hypertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, revealing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female athletes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural control response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.

Microsoft Teams meeting

Join on your computer, mobile app or room device

Click here to join the meeting

Meeting ID: 337 044 177 086
Passcode: HYAuHu

Vinsamlegast athugið að á viðburðum Háskólans í Reykjavík (HR) eru teknar ljósmyndir og myndbönd sem notuð eru í markaðsstarfi HR. Hægt er að nálgast frekari upplýsingar á eða með því að senda tölvupóst á netfangið:
Please note that at events hosted at Reykjavik University (RU), photographs and videos are taken which might be used for RU marketing purposes. Read more about this on our or send an e-mail: