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Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging

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Abstract

Alberta Stroke Program Early Computed Tomographic Scoring (ASPECTS) is a reliable method for assessing early ischemic changes in the blood supply area of the middle cerebral artery in patients with acute ischemic stroke. This study aims to propose a deep learning based automatic evaluation strategy for DWI-ASPECTS to serve as a reference for clinicians in urgent decision making for endovascular thrombectomy. Ten ASPECTS regions are extracted from the DWI series to train the independent classification network for each region, the accurate training labels of which are confirmed by neuroradiologists. Two classical convolutional neural networks (VGG-16 and ResNet-50) are validated. Subsequently, the innovative CBAM-VGG is designed to improve the accurate scoring of four small-volume DWI-ASPECTS regions, including caudate nucleus, lenticular nucleus, internal capsule, and insular lobe. Average F1-score of 0.929 and 0.840 and the average accuracy of 94.75% and 84.99% are obtained when scoring on six cortical regions M1-M6 and four small ASPECTS regions, respectively. In addition, the modified algorithm CBAM-VGG shows a significant improvement in the accuracy of estimating the four ASPECTS regions with smaller volumes. The experimental results demonstrate that the deep learning methods facilitate the efficiency and robustness of automatic DWI-ASPECTS scoring, which can provide a reference for clinical decision-making.

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Data availability

The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request pending the approval of the institution and trial/study investigators who contributed to the dataset.

Abbreviations

AIS:

Acute ischemic stroke

ASPECTS:

Alberta Stroke Program Early Computed Tomographic Scoring

AUC:

Area under the receiver operator characteristic curve

BN:

Batch normalization

C:

Caudate nucleus

CBAM:

Convolutional block attention module

Conv:

Convolutional layers

CT:

Computed tomography

DWI:

Diffusion-weighted imaging

I:

Insular lobe

IC:

Internal capsule

L:

Lenticular nucleus

MCA:

Middle cerebral artery

ROC:

Receiver operating characteristic curve

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Funding

This study has received funding by the National Natural Science Foundation of China (Grant No. 81830052), the Science and Technology Innovation Action Plan of Shanghai (Grant No. 18441900500), the Natural Science Foundation of Shanghai (Grant No. 20ZR1438300), and Shanghai Key Laboratory of Molecular Imaging (Grant No. 18DZ2260400).

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Author notes

  1. Yan Wu and Rong Sun contributed equally to this work.

Authors and Affiliations

  1. School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jun-Gong Road, Shanghai, 200093, China

    Yan Wu, Rong Sun & Shengdong Nie

  2. Medical Imaging Center, Tai’an Central Hospital, Shandong, China

    Yuanzhong Xie

Authors
  1. Yan Wu
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  2. Rong Sun
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Yan Wu and Rong Sun. The first draft of the manuscript was written by Yan Wu; all authors commented on the subsequent versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shengdong Nie.

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Ethics approval and consent to participate

An Ethics Committee and Institutional Review Board of the Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University have approved the retrospective research; thus, the requirement for subject informed consent was waived.

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Not applicable.

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The authors declare no competing interests.

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Wu, Y., Sun, R., Xie, Y. et al. Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging. Med Biol Eng Comput 61, 2149–2157 (2023). https://doi.org/10.1007/s11517-023-02867-2

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  • DOI: https://doi.org/10.1007/s11517-023-02867-2

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