TY  - JOUR
A1  - Pennig, Lenhard
A1  - Hoyer, Ulrike Cornelia Isabel
A1  - Krauskopf, Alexandra
A1  - Shahzad, Rahil
A1  - Jünger, Stephanie T.
A1  - Thiele, Frank
A1  - Laukamp, Kai Roman
A1  - Grunz, Jan-Peter
A1  - Perkuhn, Michael
A1  - Schlamann, Marc
A1  - Kabbasch, Christoph
A1  - Borggrefe, Jan
A1  - Goertz, Lukas
T1  - Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage
JF  - Neuroradiology
N2  - 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 mm3, 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 κ 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 κ 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.
KW  - aneurysms
KW  - aneurysmal subarachnoid hemorrhage
KW  - CT angiography
KW  - deep learning
KW  - convolutional neural networks
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308117
SN  - 0028-3940
SN  - 1432-1920
VL  - 63
IS  - 12
ER  -