Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with rea
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ORIGINAL ARTICLE
Usefulness of deep learning‑assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers’ performance Yuki Shinohara1 · Noriyuki Takahashi1,2 · Yongbum Lee3 · Tomomi Ohmura1 · Atsushi Umetsu4 · Fumiko Kinoshita1 · Keita Kuya5 · Ayumi Kato6 · Toshibumi Kinoshita1 Received: 12 February 2020 / Accepted: 28 April 2020 © Japan Radiological Society 2020
Abstract Purpose To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists. Materials and methods We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixeldiameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample. Results The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiveroperating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test. Conclusion The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists. Keywords Non-contrast CT · Deep learning · Hyperdense MCA sign · Hyperdense artery sign · Acute ischemic stroke
Introduction
* Yuki Shinohara shino‑y@akita‑noken.jp 1
Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels-Akita, 6‑10 Senshu‑kubota‑machi, Akita 010‑0874, Japan
2
Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Fukushima, Japan
3
Graduate School of Health Science, Niigata University, Niigata, Japan
4
Department of Diagnostic Radiology, Tohoku University School of Medicine, Sendai, Japan
5
Department of Radiology, Secomedic Hospital, Funabashi, Japan
6
Division of Radiology, Department of Pathophysiological Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
Non-contrast CT (NCCT) of the brain is a crucial diagnostic method for patients with suspected acute ischemic stroke (AIS) because it can detect intracranial hemorrhage and early ischemic changes. In the 2019 update to the 2018 guidelines for the early management of AIS from the American Heart Association and American Stroke Association, brain NCCT can be considered as a neuroimaging modality for decision making prior to intravenous recombin
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