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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
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Deep Learning Using One-stop-shop CT Scan to Predict Hemorrhagic Transformation in Stroke Patients Undergoing

Huanhuan Ren1, Haojie Song2, Jiayang Liu3

  • 1Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing 400030, China.

Academic Radiology
|October 27, 2024
PubMed
Summary

Deep learning models accurately predict hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients after reperfusion therapy using multiphase CT angiography (CTA) and CT perfusion (CTP) imaging. These models offer a reliable tool for clinical decision-making.

Keywords:
Acute ischemic strokeDeep learningHemorrhagic transformationOne-stop-shop CTReperfusion therapy

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • Hemorrhagic transformation (HT) is a significant complication after reperfusion therapy for acute ischemic stroke (AIS).
  • Accurate prediction of HT is crucial for guiding treatment decisions and improving patient outcomes.

Purpose of the Study:

  • To develop and validate deep learning (DL) models for automated prediction of HT.
  • To utilize multiphase computed tomography angiography (CTA) and computed tomography perfusion (CTP) imaging data for HT prediction.

Main Methods:

  • A multicenter retrospective study involving 229 AIS patients who underwent reperfusion therapy.
  • Development of DL models using the DenseNet architecture trained on multiphase CTA and CTP images.
  • Internal validation on 183 patients and external testing on 46 patients.

Main Results:

  • 30.1% of patients developed HT.
  • The combined CTA-CTP DL model achieved the highest predictive accuracy for HT.
  • Single-phase (arteriovenous) and single-parameter (time-to-peak) models also showed predictive capabilities.

Conclusions:

  • Deep learning models utilizing multiphase CTA and CTP images can reliably predict HT in AIS patients.
  • These validated DL models serve as a valuable tool for clinicians in managing stroke patients.
  • Automated HT prediction facilitates timely and informed treatment decisions.