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Abstract #1866

Automatic Assessment of DWI-ASPECTS for Assessment of Acute Ischemic Stroke using Recurrent Residual Convolutional Network

Luu-Ngoc Do1, Byung-Hyun Beak2, Seul-Kee Kim3, Hyung-Jeong Yang4, Woong Yoon5, and Ilwoo Park5
1Department of Radiology, Chonnam National University, Gwangju, Korea, Republic of, 2Department of Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of, 3Department of Radiology, Chonnam National University Hwasun Hospital, Gwangju, Korea, Republic of, 4Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea, Republic of, 5Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea, Republic of

The purpose of this study was to demonstrate the feasibility of using deep learning algorithms for automatic classification of DWI-ASPECTS from patients with acute ischemic stroke. DWI data from 319 patients with acute anterior circulation stroke were used to train and validate recurrent residual convolutional neural network models for binary task of classifying low- vs high- DWI-ASPECTS. Our model produced the accuracy of 84.9 ± 1.5% and the AUC of 0.925 ± 0.009, suggesting that this algorithm may provide an important ancillary tool for clinicians in a time-sensitive assessment of DWI-ASPECTS from acute ischemic stroke patients.

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