Research Progress of Deep Learning in the Diagnosis and Prevention of Stroke

Zhang, Siqi; Zhang, Miao; Ma, Shuai; Wang, Qingyong; Qu, Youyang; Sun, Zhongren; Yang, Tiansong

doi:10.1155/2021/5213550

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…It automatically extracts and represents complex features when locating the core stroke lesions in CT or MRI ( 48 ). Deep learning not only saves time and effort but also captures pixel-level information of the lesions, contributing to improved diagnostic accuracy and prognosis ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

Liang,

Feng,

Lin

et al. 2023

Front. Neurol.

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Accurately assessing the prognostic outcomes of patients with acute ischemic stroke and adjusting treatment plans in a timely manner for those with poor prognosis is crucial for intervening in modifiable risk factors. However, there is still controversy regarding the correlation between imaging-based predictions of complications in acute ischemic stroke. To address this, we developed a cross-modal attention module for integrating multidimensional data, including clinical information, imaging features, treatment plans, prognosis, and complications, to achieve complementary advantages. The fused features preserve magnetic resonance imaging (MRI) characteristics while supplementing clinical relevant information, providing a more comprehensive and informative basis for clinical diagnosis and treatment. The proposed framework based on multidimensional data for activity of daily living (ADL) scoring in patients with acute ischemic stroke demonstrates higher accuracy compared to other state-of-the-art network models, and ablation experiments confirm the effectiveness of each module in the framework.

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Section: Discussionmentioning

confidence: 99%

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

Liang,

Feng,

Lin

et al. 2023

Front. Neurol.

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“…The former can learn the hidden features of the input data, while the latter can reconstruct the original input data with the learned new features. Common applications of autoencoder contain image denoising and dimensionality reduction [ 30 ], for instance, denoising autoencoder (DAE) [ 43 ], sparse autoencoder (SAE) [ 44 ], variational autoencoder (VAE) [ 45 ], and contractive autoencoder (CAE) [ 46 ]. In ischemic stroke lesion analysis, Praveen et al proposed a stacked sparse autoencoder (SSAE) architecture for accurate segmentation of ischemic lesions from MR images and performed perfectly on the publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with an average precision of 0.968, average Dice coefficient (DC) of 0.943, and the accuracy of 0.904 [ 47 ].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Complex features from data are extracted and expressed automatically by DL when locating the stroke lesion core in CT or MRI [ 29 ]. Deep learning not only saves time and effort but also captures the pixel-level information of the lesion, which is beneficial to improve the accuracy of diagnosis and prognosis [ 30 ]. As shown in Figure 1 , the analysis of many typical deep learning models and applications of deep learning in ischemic stroke imaging is presented.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Cui

Fan

Yang

et al. 2022

BioMed Research International

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Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

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“…More standardized imaging data sets and more detailed AI experiments are needed to establish and validate the usefulness of AI in stroke imaging. Deep learning algorithms have a significant impact on stroke diagnosis, treatment, and prediction [26,27]. This study also discusses the current limitations and future development prospects of deep learning technology.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

Rajendran¹,

Radhakrishnan²,

Viswanathan³

2022

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The human brain is the body's most complicated organ. Constant blood flow is essential for the sustained functioning of the brain. A blocked blood vessel's interruption of blood supply prevents oxygen and nutrients to the brain tissues. This results in a life-threatening brain disease called Ischemic Stroke. Computed Tomography (CT) images are widely used in the diagnosis of Ischemic Stroke because of their faster acquisition and compatibility with most life support devices. CT acquired from the patients who arrived with stroke symptoms is Primary CT (PCT). After some hours CT taken for the same patient is Secondary CT (SCT). Stroke lesions may not be visible in PCT, whereas visible in SCT. Learning the features automatically using a Convolutional Neural Network (CNN) is essential to classify normal and abnormal CT slices. These networks are capable of learning the global features effectively for image classification. Though this CNN approach works, achieving desired accuracy was challenging. Different architectures considered for this CNN experimentation are VGG1, VGG2, VGG3, VGG16, InceptionV3, and ResNet50. This novel work provides a detailed explanation of the three experiments conducted using PCT and SCT slices. Three experiments are conducted using SCT and PCT slices. The pretrained VGG16, ResNet50, and InceptionV3 networks with the ImageNet database are applied as a first approach. Both SCT and PCT slices are used for testing alone. It resulted in 49.22%, 47.076% and 49.36% classification accuracy. In the second approach, different models were trained for classification from PCT and SCT slices. This includes the networks like VGG1, VGG2, VGG3, VGG16, VGG16 with dropout, ResNet, ResNet50 with lambda regularization, InceptionV3, and InceptionV3 with lambda regularization. The accuracies achieved are 68%, 69.4%, 72%, 78.2%, 79.1%, 77%, 77.8%, 79.6% and 80.1%. The accuracy was improved with dropout and lambda regularization. The networks with high accuracy are selected and an ensemble model is developed as a third approach. ResNet50, VGG16, and InceptionV3 are combined to form an ensemble network. This ensemble network yielded an accuracy of 81.98% when SCT and PCT slices are used for both training and testing. And produced 74% accuracy when PCT slices alone were used. Also produced 93.76% accuracy when SCT slices alone were used.

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Research Progress of Deep Learning in the Diagnosis and Prevention of Stroke

Cited by 17 publications

References 18 publications

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

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