Prediction of Ischemic Stroke in MRI Images based on Machine Learning: A Systematic Review

Ziwei He

doi:10.62051/qtf5k466

Authors

Ziwei He

DOI:

https://doi.org/10.62051/qtf5k466

Keywords:

Ischemic Stroke; deep learning; MRI.

Abstract

Ischemic stroke is a dangerous disease that endangers human health and is one of the important causes of disability and death. Early detection and prediction of ischemic stroke are essential for timely intervention and prevention of serious consequences. Fortunately, deep learning-based medical image research has made great progress, which can assist doctors in diagnosing conditions. This paper analyzes the overall effectiveness of existing machine learning (ML) methods in predicting final infarction from baseline imaging. First, this paper searched 11 relevant studies and analyzed the impact of these methods on patients with acute ischemic stroke (AIS). Then, the performance, effect and characteristics of the model are analyzed. Although these methods are generally good at predicting infarction, they show great potential. Based on machine learning methods, reliance on abundant data is essential for effective learning. Insufficient data can lead to significant errors in model predictions. Therefore, future research should focus on collecting ample clinical data for model training to enhance both accuracy and robustness.

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