Ph.D. Thesis Defense - Marco Recenti
Title: ADVANCING CLINICAL EVALUATION AND DIAGNOSTICS WITH ARTIFICIAL INTELLIGENCE TECHNOLOGIES
ADVANCING CLINICAL EVALUATION AND DIAGNOSTICS WITH ARTIFICIAL INTELLIGENCE
TECHNOLOGIES
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
Abstract:
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.
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