Etiologies of Ischemic Stroke in Sub-Saharan Africa, Case of Benin

Agbétou, Mendinatou; Sowanou, Arlos; Goudjinou, Gérard; Adjien, Constant; Houinato, Dismand Stéphan; Gnonlonfoun, Dieudonné

doi:10.4236/nm.2020.114012

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Wastages from other parts of the body obstruct the blood flow via the afflicted artery during an embolic event [6]. This results in cerebral hypertension, or a shortage of oxygen in the brain, which leads to brain tissue death or ischemic stroke [7], [8]. When a patient comes to the hospital after trauma, the emergency department would assess the patient.…”

Section: Introductionmentioning

confidence: 99%

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Ali

Abdullah

Saad

et al. 2023

IJECE

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<span lang="EN-US">Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and <br /> intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation.</span>

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Section: Introductionmentioning

confidence: 99%

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Ali

Abdullah

Saad

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“…Recent epidemiological data reveal the gravity of the situation in sub-Saharan Africa. Stroke is the leading cause of acquired disability in adults [1] [4] [5]. Based on available world health organisation (WHO) data, the annual number of strokes worldwide will drop from 16 million in 2005 to 23 million by 2030, due to the aging of the population [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Warning Signs by the Patient with Ischemic Stroke and Impact on Emergency Department Admission Times

Serge¹,

Aristide²,

Cristella³

et al. 2021

OJEM

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The general objective of this study was to assess the impact of the recognition of warning, signs of ischemic stroke, on the reduction of emergency admission times and the improvement of the prognosis of the patient suffering from ischemic stroke in the emergency department of the Yaounde central hospital. Methods: This was a prospective study with a descriptive and analytical aim carried out in the medical emergency department of the central hospital of Yaounde over a period of 3 months, going from October to December 2020. All patients admitted to emergency departement, during this period, for a diagnosis of ischemic stroke confirmed by a brain CT scan were included in the study. The data was collected on a survey sheet divided into 3 sections: the patient, the assessment of his knowledge and his reaction to the stroke. The variables studied were socio-demographic data, cardiovascular risk factors and warning signs of stroke. Data analysis was conducted with Census and Survey Processing System (CSPRO). Results: During the study period, 62 patients met the inclusion criteria. Adults under 50 (41.94%) were the most represented age group. The mean age of the patients was 52.3 years with ranges ranging from 36 to 82 years. The sex ratio was 1.38 in favor of women. The most common unmodifiable cardiovascular risk factor was age over 55 years 44%. High blood pressure 61% was the most common modifiable risk factor. More than half of the study population had no knowledge of How to cite this paper:

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Automated Classification of Collateral Circulation for Ischemic Stroke in Cone-Beam CT Images Using VGG11: A Deep Learning Approach

Ali,

Abdullah,

Saad

et al. 2024

BioMedInformatics

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Background: Ischemic stroke poses significant challenges in diagnosis and treatment, necessitating efficient and accurate methods for assessing collateral circulation, a critical determinant of patient prognosis. Manual classification of collateral circulation in ischemic stroke using traditional imaging techniques is labor-intensive and prone to subjectivity. This study presented the automated classification of collateral circulation patterns in cone-beam CT (CBCT) images, utilizing the VGG11 architecture. Methods: The study utilized a dataset of CBCT images from ischemic stroke patients, accurately labeled with their respective collateral circulation status. To ensure uniformity and comparability, image normalization was executed during the preprocessing phase to standardize pixel values to a consistent scale or range. Then, the VGG11 model is trained using an augmented dataset and classifies collateral circulation patterns. Results: Performance evaluation of the proposed approach demonstrates promising results, with the model achieving an accuracy of 58.32%, a sensitivity of 75.50%, a specificity of 44.10%, a precision of 52.70%, and an F1 score of 62.10% in classifying collateral circulation patterns. Conclusions: This approach automates classification, potentially reducing diagnostic delays and improving patient outcomes. It also lays the groundwork for future research in using deep learning for better stroke diagnosis and management. This study is a significant advancement toward developing practical tools to assist doctors in making informed decisions for ischemic stroke patients.

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Etiologies of Ischemic Stroke in Sub-Saharan Africa, Case of Benin

Cited by 3 publications

References 14 publications

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Recognition of Warning Signs by the Patient with Ischemic Stroke and Impact on Emergency Department Admission Times

Automated Classification of Collateral Circulation for Ischemic Stroke in Cone-Beam CT Images Using VGG11: A Deep Learning Approach

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