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Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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To investigate putative interacting or distinct pathways for hippocampal complex substructure (HCS) atrophy and cognitive affection in early-stage Alzheimer's disease (AD) and cerebrovascular disease (CVD), we recruited healthy controls, patients with mild cognitive impairment (MCI) and poststroke patients. HCSs were segmented, and quantitative white-matter hyperintensity (WMH) load and cerebrospinal fluid (CSF) amyloid-β concentrations were determined. The WMH load was higher poststroke. All examined HCSs were smaller in amyloid-positive MCI than in controls, and the subicular regions were smaller poststroke. Memory was reduced in amyloid-positive MCI, and psychomotor speed and executive function were reduced in poststroke and amyloid-positive MCI. Size of several HCS correlated with WMH load poststroke and with CSF amyloid-β concentrations in MCI. In poststroke and amyloid-positive MCI, neuropsychological function correlated with WMH load and hippocampal volume. There are similar patterns of HCS atrophy in CVD and early-stage AD, but different HCS associations with WMH and CSF biomarkers. WMHs add to hippocampal atrophy and the archetypal AD deficit delayed recall. In line with mounting evidence of a mechanistic link between primary AD pathology and CVD, these additive effects suggest interacting pathologic processes.

Our method is able to analyse LSCD in a wide set of semantic categories throughout the progression of CI, being a valuable first screening method in AD diagnosis in its early stages. Because of its low cost, it can be used for routine clinical evaluations or screenings to detect AD in its early stages. Besides, due to its knowledge-based structure, it can be easily extended to provide an explanation of the diagnosis and to the study of other neurodegenerative diseases. Further, this is a key advantage of BNs over other machine learning methods with similar performance: it is a recognisable and explanatory model that allows one to study irregularities in different semantic categories.