Machine Learning in Acute Ischemic Stroke Neuroimaging

Kamal, Haris; López, Víctor A.; Sheth, Sunil A

doi:10.3389/fneur.2018.00945

Cited by 102 publications

(72 citation statements)

References 62 publications

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“…To summarize detailed information such as machine learning approaches, sample size, inputted features types and reported accuracy. Kamal et al (2018) [8] Machine learning in acute ischemic stroke neuroimaging 10 Systematic review…”

Section: -2018mentioning

confidence: 99%

“…[14] Machine [15] Human brain study using AI 6317 bibliometric analysis from 19 to 350). Besides, the existing reviews focus on narrowed and particular topics, for example, deep learning approaches for glioma imaging [7], machine learning in acute ischemic stroke [8], and AI in stroke imaging [6], failing to provide a general overview of the community of AI-enhanced human brain research. In addition, these qualitative reviews on specific topics or bibliometric analyses based primarily on metadata of scientific publications (e.g., year of publication or citation index) cannot accommodate the wide and fast-growing research and application scopes of modern AI-assisted human brain research.…”

Section: -2019mentioning

confidence: 99%

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Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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Section: -2018mentioning

confidence: 99%

Section: -2019mentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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“…Recent machine learning techniques, such as deep learning, have been used in the field of cerebrovascular disorders [7] and have the potential to solve the important problem of outcome prediction in acute ischemic stroke [8]. Machine learning models have been created to predict the outcome after reperfusion therapy using neuroimaging [9]; however, most of these studies focused on the prediction of lesion outcome as compared to clinical outcome [10]. We focused only on clinical outcome prediction using multi-parametric MRI imaging data acquired at the acute stage before any treatment decision, as it is the preferred imaging for diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

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“…There are several attempts which can be enumerated for deciding a patient with an acute ischemic stroke. These are usually intensive and lack of robustness due to the unknown stroke symptom onset [26]. Therefore, Pereira et al [19] employ a CNN classifier for brain ischemic CT images by combining with Particle Swarm Optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Suberi¹,

Nurshazwani²,

Tomari³

et al. 2019

IJACSA

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Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis.

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Machine Learning in Acute Ischemic Stroke Neuroimaging

Cited by 102 publications

References 62 publications

Topics and trends in artificial intelligence assisted human brain research

Topics and trends in artificial intelligence assisted human brain research

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

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

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