Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke
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 automate...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40564-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850179769784598528 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Gianluca Brugnara |
| collection | DOAJ |
| description | 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 <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.ccibonn.ai/ ) 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. |
| format | Article |
| id | doaj-art-24a3d137218a42cfa5b7e7a9db4adb74 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-24a3d137218a42cfa5b7e7a9db4adb742025-08-20T02:18:25ZengNature PortfolioNature Communications2041-17232023-08-0114111510.1038/s41467-023-40564-8Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic strokeGianluca Brugnara0Michael Baumgartner1Edwin David Scholze2Katerina Deike-Hofmann3Klaus Kades4Jonas Scherer5Stefan Denner6Hagen Meredig7Aditya Rastogi8Mustafa Ahmed Mahmutoglu9Christian Ulfert10Ulf Neuberger11Silvia Schönenberger12Kai Schlamp13Zeynep Bendella14Thomas Pinetz15Carsten Schmeel16Wolfgang Wick17Peter A. Ringleb18Ralf Floca19Markus Möhlenbruch20Alexander Radbruch21Martin Bendszus22Klaus Maier-Hein23Philipp Vollmuth24Department of Neuroradiology, Heidelberg University HospitalDivision of Medical Image Computing, German Cancer Research Center (DKFZ)Department of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Bonn University HospitalDivision of Medical Image Computing, German Cancer Research Center (DKFZ)Division of Medical Image Computing, German Cancer Research Center (DKFZ)Division of Medical Image Computing, German Cancer Research Center (DKFZ)Department of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalNeurology Clinic, Heidelberg University HospitalDepartment of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of HeidelbergDepartment of Neuroradiology, Bonn University HospitalInstitute for Applied Mathematics, University of BonnDepartment of Neuroradiology, Bonn University HospitalNeurology Clinic, Heidelberg University HospitalNeurology Clinic, Heidelberg University HospitalDivision of Medical Image Computing, German Cancer Research Center (DKFZ)Department of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Bonn University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDivision of Medical Image Computing, German Cancer Research Center (DKFZ)Department of Neuroradiology, Heidelberg University HospitalAbstract 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.ccibonn.ai/ ) 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.https://doi.org/10.1038/s41467-023-40564-8 |
| spellingShingle | 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 Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke Nature Communications |
| title | Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |
| title_full | Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |
| title_fullStr | Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |
| title_full_unstemmed | Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |
| title_short | Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke |
| title_sort | deep learning based detection of vessel occlusions on ct angiography in patients with suspected acute ischemic stroke |
| url | https://doi.org/10.1038/s41467-023-40564-8 |
| work_keys_str_mv | AT gianlucabrugnara deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT michaelbaumgartner deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT edwindavidscholze deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT katerinadeikehofmann deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT klauskades deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT jonasscherer deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT stefandenner deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT hagenmeredig deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT adityarastogi deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT mustafaahmedmahmutoglu deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT christianulfert deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT ulfneuberger deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT silviaschonenberger deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT kaischlamp deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT zeynepbendella deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT thomaspinetz deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT carstenschmeel deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT wolfgangwick deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT peteraringleb deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT ralffloca deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT markusmohlenbruch deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT alexanderradbruch deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT martinbendszus deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT klausmaierhein deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke AT philippvollmuth deeplearningbaseddetectionofvesselocclusionsonctangiographyinpatientswithsuspectedacuteischemicstroke |