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Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese

Saira Osama1, Kashif Zafar1, Muhammad Usman Sadiq1

  • 1Department of Computer Science, National University of Computing and Emerging Sciences, 852-B Milaad St, Block B Faisal Town, Lahore 54000, Pakistan.

Diagnostics (Basel, Switzerland)
|October 27, 2020
PubMed
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A new deep learning model, the parallel multi-parametric feature embedded siamese network (PMFE-SN), effectively predicts stroke treatment outcomes from limited, imbalanced MRI data. This approach improves accuracy for both common and rare stroke cases.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Stroke is a leading global cause of death and disability, with ischemic stroke being the most prevalent type.
  • Multi-parametric magnetic resonance imaging (MRI) is crucial for acute stroke diagnosis, lesion characterization, and assessing reperfusion therapy efficacy.
  • Predicting treatment outcomes is challenging due to variability in stroke characteristics and limitations of current diagnostic datasets, which are often scarce and imbalanced.

Purpose of the Study:

  • To develop an automated model for predicting stroke treatment outcomes using multi-parametric MRI data.
  • To address the challenges of limited sample sizes and high class imbalance in acute-stage stroke imaging datasets.
  • To create a valuable tool for clinicians to better assess risks and benefits of reperfusion therapies.
Keywords:
acute ischemic strokedeep learningfeature embeddingimbalancemachine learningmulti-parametric MRIsiamese network

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Main Methods:

  • Introduction of the parallel multi-parametric feature embedded siamese network (PMFE-SN), a novel deep learning architecture.
  • Implementation of PMFE-SN designed to learn effectively from few samples and handle skewed multi-parametric MRI data.
  • Definition and application of five evaluation metrics robust to class imbalance for performance assessment.

Main Results:

  • The PMFE-SN model demonstrated superior performance across all defined metrics compared to state-of-the-art techniques.
  • The model achieved significant predictive accuracy for both minority (0.67) and majority (0.61) classes, even with minimal training data.
  • In contrast, traditional methods using handcrafted features yielded 0 accuracy for the minority class and 0.33 for the majority class.

Conclusions:

  • The proposed PMFE-SN is highly effective for predicting stroke treatment outcomes from imbalanced, limited multi-parametric MRI data.
  • This deep learning approach offers a promising solution for clinical decision-making in acute stroke management.
  • PMFE-SN advances the field by enabling reliable predictions even with scarce data, overcoming a major hurdle in stroke imaging research.