AIM

 AIM - Artificial Intelligence in Medicine

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Analysis techniques based of Artificial Intelligence, including machine learning and, more recently, deep-learning approaches, are widely used in medical diagnostics and therapy. Starting from sensor data processing for image reconstruction, specific solutions include a variety of data mining, image segmentation, annotation and analysis applications, to end with intelligent systems for image-guided therapy and computed-aided diagnosis.

Big companies are quickly developing and placing on the market intelligent systems applications to assist clinicians in their daily tasks. Nevertheless, research institutions can provide relevant contributions in this still-open field of research. In particular, INFN can take advantage of its unique expertise in big data handling inherited for high-energy physics experiments and to the availability of extremely powerful computing centres mainly built to store and process those data. More importantly, a network of fruitful interactions between INFN Physicists and Clinicians of several Italian hospitals and clinical research centers has been built in the last two decades, thanks also to specific research initiatives funded by INFN-CSN5. 

 The Artificial Intelligence in Medicine (AIM) project aims to exploit the expertise of INFN and associated researchers on medical data processing and enhancement, and turn it in the development of advanced and effective analysis instruments to be eventually clinically validated and translated into products.

INFN Section of Catania is working on predictive models for transcranial neurosurgery through focused ultrasounds guided by magnetic resonance (tc-MRgFUS).

The study concerns patients with medication refractory ET. The use of advanced magnetic resonance (MR) techniques (such as diffusion weighted imaging as well as function MRI) will allows for a further improvement of the therapy and a thorough quantitative evaluation of the effects of the treatment with Trans-cranial MRgFUS. In the first step of the activity intends to create a common database collecting images and clinical data of medication refractory ET patient in collaboration with the Azienda Ospedaliera Universitaria Policlinico of Palermo. These data will be analyzed with ad-hocdeveloped softwares for the selection of the most important features as well as for a faster spatial localization of the target in the thalamus (to speed treatment procedure).  We propose to employ classifiers to predict predict the overall tcMRgFUS treatment response, patient-specific effectiveness and side-effects.