Modelli di Intelligenza Artificiale per l'analisi di dati da neuroimaging multimodale

Medical imaging (MI) refers to several technologies that provide images of organs and tissues of human body for diagnosis and scientific purposes. Furthermore, the technologies that allow us to capture medical images and signals are advancing rapidly, providing higher quality images of previously unmeasured biological features at decreasing costs. This has mainly occurred for highly specialized applications, such as cardiology and neurology. Artificial Intelligence (AI), which to date has largely focused on non medical applications, such as computer vision, provides to be an instrumental toolkit that will help unleash the potential of MI. In fact, the significant variability in anatomy across individuals, the lack of specificity of the imaging techniques, the unpredictability of the diseases, the weakness of the biological signals, the presence of noise and artifacts and the complexities of the underlying biology often make it impossible to derive deterministic algorithmic solutions for the problems encountered in neurology. Aim of this thesis was to develop AI models capable of carrying out quantitative, objective, accurate and reliable analyzes of imaging tools, EEG and MRI, used in neurology. Beyond the development of AI models, attention was focused on the quality of data which can be lowered by the "uncertainty" produced by the issues cited above. Further, the uncertainty affecting data was also described, discussed and addressed. Main results have been the proposal of innovative AI-based strategies for signal and image improvement through artifact reduction and data stabilization both in EEG and in MRI. This has allowed to apply EEG for weak signals recognition and interpretation (infant 3M patients), to provide effective strategies for dealing MRI variability and uncertainty in multiple sclerosis segmentation, both for single source and multiple-source MRI. According to the used evaluation criteria, the obtained results are comparable with those obtained by human experts. Future developments will regard the generalization of the proposed strategies to cope with different diseases or with different applications of MI. Particular attention will be paid to the optimization of the models and to understand the processes underlying their behavior. To this aim, specific strategies for checking the deep structures of the proposed architectures will be studied. In this way, besides model optimization, it would be possible to get the functional relationships among the features generating from the model and use them to improve human knowledge (a sort of inverse transfer learning).