Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension.

Portal hypertension is a complex medical condition characterized by elevated blood pressure in the portal venous system. The conventional diagnosis of such disease often involves invasive procedures such as liver biopsy, endoscopy, or imaging techniques with contrast agents, which can be uncomfortable for patients and carry inherent risks. This study presents a deep neural network method in support of the non-invasive diagnosis of portal hypertension in patients with chronic liver diseases.

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence.

Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%.

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators.

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem.

Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.

Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated.

Estimation of brain connectivity through Artificial Neural Networks.

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors.