Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy.

Jean DarcourtWaleed BrinjikjiOlivier FrançoisAlice GiraudCollin R JohnsonSmita PatilSenna StaessensRamanathan D KadirvelMahmoud H MohammadenLeonardo PisaniGabriel Martins RodriguesNicole M CancelliereVitor Mendes PereiraFranz BozsakKaren M DoyleSimon F De MeyerPierluca Messina David F Kallmes Christophe CognardRaul G Nogueira

Published in: Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences (2023)

Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

Keyphrases

acute ischemic stroke
machine learning
high resolution
red blood cell
artificial intelligence
ionic liquid
magnetic resonance
molecularly imprinted