Stroke is one of the leading causes of death and a major contributor to chronic disability worldwide, with Ischemic stroke accounting for 80% of cases. Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Annually, there are over 7.6 million new cases of Ischemic stroke. Additionally, the stroke mortality rate is 42.56 per 100,000 resulting in approximately 3.3 million deaths from ischemic stroke each year. Based on stroke incidence, prevalence, and mortality rates, the estimated total medical stroke-related costs are projected to double from $36.7 billion to $94.3 billion between 2015 and 2035. Early detection of Ischemic stroke is crucial to start thrombolytic therapy, intravenous tissue plasminogen activator (tPA). The tPA infusion, the first-line treatment for acute ischemic stroke, has a limited treatment window of 3–4.5 hours after stroke onset, as recommended by the American Heart Association/American Stroke Association. This is because the tPA effectiveness is highly time-dependent due to the sensitivity of brain tissue to Ischemia. To reduce the mortality and morbidity of stroke, the use of advanced technology machine learning (ML), has been studied to aid healthcare providers in attempting to detect an ischemic stroke early in clinical settings. Below are some of the findings as per a study by Suebsarn et al.
Deep Convolutional Neural Networks (DCNNs): DCNNs were the most frequently utilized algorithm in the study for ischemic stroke diagnosis, highlighted in three studies, representing 33.33% of the total. DCNNs are well-suited for analyzing visual imagery, making them highly effective for pattern recognition in medical images. The combination of Faster R-CNN, YOLOV3, and SSD algorithms within the DCNN model, except in studies by Zhang et al., indicates a sophisticated approach to enhancing the DCNN’s capability to detect intricate patterns in CT or MRI scans crucial for diagnosing ischemic stroke.
Three-Dimensional Convolutional Neural Networks (3D-CNNs): Used in two studies (22.22%), 3D-CNNs extend the capabilities of traditional CNNs by analyzing data in three dimensions. This feature is particularly beneficial for medical imaging like CT or MRI scans, where the depth of the image (the third dimension) provides critical information. These models are adept at handling dynamic sequences and volumetric data, making them ideal for identifying abnormalities within the complex structures of the brain that signify ischemic stroke.
Two-stage Deep Convolutional Neural Networks (Two-stage DCNNs): Also utilized in two studies (22.22%), Two-stage DCNNs are designed to first identify regions of interest within an image or video and then classify the identified regions at a higher resolution. This approach allows for precise localization and diagnosis of ischemic stroke by focusing on specific areas within the brain scans that exhibit signs of stroke, enhancing both accuracy and efficiency.
Generalized Local Higher-order Singular Value Decomposition Denoising Algorithm (GL-HOSVD): This algorithm, used in one study (11.11%), focuses on noise reduction in multidimensional data. In the context of ischemic stroke diagnosis, GL-HOSVD can enhance the clarity of medical images by removing artifacts and noise that often obscure critical details. This improvement in image quality is crucial for accurate diagnosis, especially in detecting subtle signs of ischemic stroke that might be missed in noisier images.
Advanced Deconvolution Network Model (AD-CNNnet): Implemented in one study (11.11%), the AD-CNNnet is specifically designed for image restoration tasks. This model can reconstruct high-quality images from degraded inputs, making it highly valuable for diagnosing ischemic stroke where the quality of medical imaging can significantly impact the detection and interpretation of stroke indicators.
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