Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke
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 detecti...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
15 August 2023
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| In: |
Nature Communications
Year: 2023, Volume: 14, Pages: 1-15 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-023-40564-8 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41467-023-40564-8 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41467-023-40564-8 
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| Author Notes: | 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, Philipp Vollmuth |
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| 245 | 1 | 0 | |a Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |c 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, Philipp Vollmuth |
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| 520 | |a 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 <2 minutes. Pseudo-prospective 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 (p ≤ 0.003 each). 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, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms. | ||
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