Universität zu Köln

Pennig, Lenhard, Hoyer, Ulrike Cornelia Isabel, Krauskopf, Alexandra, Shahzad, Rahil, Juenger, Stephanie T., Thiele, Frank, Laukamp, Kai Roman, Grunz, Jan-Peter, Perkuhn, Michael, Schlamann, Marc, Kabbasch, Christoph, Borggrefe, Jan ORCID: 0000-0003-2908-7560 and Goertz, Lukas (2021). Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage. Neuroradiology, 63 (12). S. 1985 - 1995. NEW YORK: SPRINGER. ISSN 1432-1920

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Abstract

Purpose To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). Methods Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 +/- 185.4 mm(3), 13.0% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. Results In the test set, the detection sensitivity of the DLM-Ens (85.7%) was comparable to the radiologists (reader 1: 91.2%, reader 2: 86.5%, and reader 3: 86.5%; Fleiss kappa of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6%, reader 2: 97.6%,and reader 3: 96.0%; overall P=.024; Fleiss kappa of 0.878), especially for secondary aneurysms (88.2% of the additional aneurysms provided by the DLM). Conclusion Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pennig, Lenhard UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoyer, Ulrike Cornelia Isabel UNSPECIFIED UNSPECIFIED UNSPECIFIED Krauskopf, Alexandra UNSPECIFIED UNSPECIFIED UNSPECIFIED Shahzad, Rahil UNSPECIFIED UNSPECIFIED UNSPECIFIED Juenger, Stephanie T. UNSPECIFIED UNSPECIFIED UNSPECIFIED Thiele, Frank UNSPECIFIED UNSPECIFIED UNSPECIFIED Laukamp, Kai Roman UNSPECIFIED UNSPECIFIED UNSPECIFIED Grunz, Jan-Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Perkuhn, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Schlamann, Marc UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Borggrefe, Jan UNSPECIFIED orcid.org/0000-0003-2908-7560 UNSPECIFIED Goertz, Lukas UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-591492
DOI:	10.1007/s00234-021-02697-9
Journal or Publication Title:	Neuroradiology
Volume:	63
Number:	12
Page Range:	S. 1985 - 1995
Date:	2021
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-1920
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Clinical Neurology; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59149