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Published in:
2024 | OriginalPaper | Chapter
Abstract: Deep Learning-based Detection of Vessel Occlusions on CT-Angiography in Patients with Suspected Acute Ischemic Stroke
Authors : Gianluca Brugnara, Michael Baumgartner, Edwin D. Scholze, Katerina Deike-Hofmann, Klaus Kades, Jonas Scherer, Stefan Denner, Hagen Meredig, Aditya Rastogi, Mustafa A. 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, Philipp Vollmuth
Published in: Bildverarbeitung für die Medizin 2024
Publisher: Springer Fachmedien Wiesbaden
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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 artifical neural network (ANN) which allows automated detection of abnormal vessel findings. Pseudoprospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25–45% for sensitivity and 4–11% for NPV.We provide an imaging platform (https://stroke.neuroAI-HD.org) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing. Notably, this work has previously been published in Nature Communications [1].