A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies

Abstract

Multimodal neuroimaging analyses are of major interest for both research and clinical practice, enabling the combined evaluation of the structure and function of the human brain. These analyses generate large volumes of data and consequently increase the amount of possibly useful information. Indeed, BrainArchive was developed in order to organize, maintain and share this complex array of neuroimaging data. It stores all the information available for each participant/patient, being dynamic by nature. Notably, the application of reasoning systems to this multimodal data has the potential to provide tools for the identification of undiagnosed diseases. As a matter of fact, in this work we explore how Artificial Intelligence techniques for decision support work, namely CaseBased Reasoning (CBR) that may be used to achieve such endeavour. Particularly, it is proposed a reasoning system that uses the information stored in BrainArchive as past knowledge for the identification of individuals that are at risk of contracting some brain disease.

Trends in Research on Artificial Intelligence in Anesthesia: A VOSviewer -Based Bibliometric Analysis

“…The application of AI methods toward clinical decision support has been demonstrated to be effective in many fields of medicine [3,4], including clinical anesthesia [5]. In particular, AI has been investigated for stratification of the risks of anesthesia and event prediction, depth of anesthesia monitoring, anesthesia management (e.g., closed-loop anesthesia), ultrasound-based procedures (nerve blocks and vascular access), pain management, operating room logistics, and other aims [6,7].…”