Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Gianluca Brugnara, Michael Baumgartner, Edwin David Scholze, Katerina Deike-Hofmann, Klaus Kades, Jonas Scherer, Stefan Denner, Hagen Meredig, Aditya Rastogi, Mustafa Ahmed Mahmutoglu, Christian Ulfert, Ulf Neuberger, Silvia Schönenberger, Kai Schlamp, Zeynep Bendella, Thomas Pinetz, Carsten Schmeel, Wolfgang Wick, Peter A. Ringleb, Ralf Floca, Markus Möhlenbruch, Alexander Radbruch, Martin Bendszus, Klaus Maier-Hein and Philipp Vollmuth ()
Additional contact information
Gianluca Brugnara: Heidelberg University Hospital
Michael Baumgartner: German Cancer Research Center (DKFZ)
Edwin David Scholze: Heidelberg University Hospital
Katerina Deike-Hofmann: Bonn University Hospital
Klaus Kades: German Cancer Research Center (DKFZ)
Jonas Scherer: German Cancer Research Center (DKFZ)
Stefan Denner: German Cancer Research Center (DKFZ)
Hagen Meredig: Heidelberg University Hospital
Aditya Rastogi: Heidelberg University Hospital
Mustafa Ahmed Mahmutoglu: Heidelberg University Hospital
Christian Ulfert: Heidelberg University Hospital
Ulf Neuberger: Heidelberg University Hospital
Silvia Schönenberger: Heidelberg University Hospital
Kai Schlamp: Thoraxklinik at University of Heidelberg
Zeynep Bendella: Bonn University Hospital
Thomas Pinetz: University of Bonn
Carsten Schmeel: Bonn University Hospital
Wolfgang Wick: Heidelberg University Hospital
Peter A. Ringleb: Heidelberg University Hospital
Ralf Floca: German Cancer Research Center (DKFZ)
Markus Möhlenbruch: Heidelberg University Hospital
Alexander Radbruch: Bonn University Hospital
Martin Bendszus: Heidelberg University Hospital
Klaus Maier-Hein: German Cancer Research Center (DKFZ)
Philipp Vollmuth: Heidelberg University Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in

Date: 2023
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DOI: 10.1038/s41467-023-40564-8

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