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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
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DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using

Shih-Yen Lin1, Pi-Ling Chiang2, Meng-Hsiang Chen2

  • 1Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Neuroimage. Clinical
|May 24, 2023
PubMed
Summary

A new deep learning model, DGA3-Net, accurately detects early signs of acute ischemic stroke (AIS) on non-contrast computerized tomography (NCCT) scans. This AI tool assists in rapid Alberta Stroke Program Early CT Score (ASPECTS) assessment, improving diagnostic speed and accuracy.

Keywords:
Acute ischemic strokeAlberta Stroke Program Early CT Score (ASPECTS)Convolutional neural networkDeep learningNon-contrast computerized tomography

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Early detection of acute ischemic stroke (AIS) using non-contrast computerized tomography (NCCT) is critical for timely treatment.
  • Identifying subtle hypoattenuation and performing accurate Alberta Stroke Program Early CT Score (ASPECTS) assessments on NCCT are challenging and time-consuming for human experts.

Purpose of the Study:

  • To develop and validate a novel convolutional neural network (CNN)-based model, DGA3-Net, for automated ASPECTS assessment by detecting early ischemic changes in NCCT scans.
  • To improve the accuracy and efficiency of AIS diagnosis from NCCT.

Main Methods:

  • DGA3-Net utilizes a parameter-efficient dihedral group CNN encoder, attention-guided slice aggregation, and an asymmetry-aware classifier.
  • The model was trained and validated on retrospective NCCT datasets from suspected AIS patients (n=170 primary, n=90 external).
  • Performance was evaluated against expert neuroradiologist assessments.

Main Results:

  • DGA3-Net demonstrated superior performance compared to two expert neuroradiologists in both regional stroke identification (F1=0.69) and ASPECTS evaluation (Cohen's Kappa=0.70).
  • Ablation studies confirmed the effectiveness of the model's design components.
  • Visualization techniques showed that the model focused on clinically relevant imaging signs.

Conclusions:

  • Deep learning, specifically the DGA3-Net model, shows significant potential for timely and accurate AIS diagnosis from NCCT scans.
  • This AI-driven approach can enhance the quality of care for AIS patients by potentially reducing diagnostic time and improving assessment accuracy.