Deep Learning for Prediction of Mechanism in Acute Ischemic Stroke Using Brain MRI

Park, Seung; Kim, Baik-Kyun; Han, Min Hyun; Hong, Jeong‐Ho; Yum, Kyu Sun; Lee, Dae-In

doi:10.21203/rs.3.rs-604141/v1

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…21 Another study reported that a deep learning algorithm to classify stroke subtypes using DWI showed an average accuracy of 81.9%. 22 However, these investigations did not conduct an external validation. As described, the present study validated our deep learning algorithm in three different external datasets and in a clinical trial involving two hospitals.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

Ryu,

Schellingerhout,

Lee

et al. 2024

Preprint

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BACKGROUND: Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted imaging (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS: Model training, validation, and internal testing were done in 2,988 patients with acute ischemic stroke from three stroke centers by using U-net for infarct segmentation and EfficientNetV2 for stroke subtype classification. Experienced vascular neurologists (n=5) determined stroke subtypes for external test datasets, while establishing a consensus for clinical trial datasets using the TOAST classification. Infarcts on DW images were automatically segmented using an artificial intelligence solution that we recently developed, and their masks were fed as inputs to a deep learning algorithm (DWI-only algorithm). Subsequently, another model was trained, with the presence or absence of AF included in the training as a categorical variable (DWI+AF algorithm). These models were tested: a) internally against the opinion of the labeling experts, b) against fresh external DWI data, and also c) against clinical trial DWI data acquired at a later date. RESULTS: In the training-and-validation datasets, the mean age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only algorithm and the DWI+AF algorithm respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3-60.7% and 73.7-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen?s kappa were respectively 58.1% and 0.34 for the DWI-only algorithm vs. 72.9% and 0.57 for the DWI+AF algorithm. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSIONS: Our deep learning algorithm trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes as accurately as a consensus of stroke experts.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

Ryu,

Schellingerhout,

Lee

et al. 2024

Preprint

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“…Batch normalization is used frequently throughout the model and is applied to activation inputs. In addition to digging deeper, the researchers devised a revolutionary technique called the Inception module [29]. Multiple feature extraction, adaptable filter size and extracting features at varied scales are the advantage of InceptionV3.…”

Section: Inceptionv3mentioning

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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“…Complex motor damage can result when PD and CVD are linked to the development and phenotype of PD [24,26]. Capillary segmentation and associated damage to the capillary network in diverse areas of the brain are caused by vascular abnormalities [27].…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Suri¹,

Sauseng

Maindarkar

et al. 2022

Metabolites

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Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

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Deep Learning for Prediction of Mechanism in Acute Ischemic Stroke Using Brain MRI

Cited by 5 publications

References 29 publications

Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

Deep learning-based automatic classification of ischemic stroke subtype using diffusion-weighted images

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

Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

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