Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network

Yu-jia, Mei; Hu, Ruiting; Jia, Lin; Xu, Hongyu; Wu, Lei; Li, He-peng; Ye, Zi‐Ming; Qin, Chao

doi:10.1016/j.wneu.2022.01.068

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…In regard to LAAS prevention, Deep CNN has been successfully employed in carotid plaque ultrasound evaluation in order to predict plaque tissue rupture risk [ 21 ]. In addition, the application of CNN for an inexpensive exam like an intracranial ultrasound for the recognition of intracerebral stenosis demonstrated good sensitivity and specificity, overcoming the operator-dependent problems closely related to ultrasound examination [ 22 ]. In addition, several studies have employed ML in CTA imaging interpretation to detect stroke and large vessel occlusion [ 23 , 24 ].…”

Section: Ischemic Stroke and Artificial Intelligence: Are You A Bot? ...mentioning

confidence: 99%

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

et al. 2023

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The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

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Section: Ischemic Stroke and Artificial Intelligence: Are You A Bot? ...mentioning

confidence: 99%

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

et al. 2023

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“…Similarly, 92.82% sensitivity was achieved by using U-Net structure to detect intracranial aneurysms in DSA images [14]. Transcranial Doppler images were analyzed and VGG16 was administered to detect aneurysm in the Middle Cerebral Artery (MCA) to achieve 84% sensitivity [15]. Region-Based Convolutional Neural Network (RCNN) model was used for automatic detection of aneurysms and the sensitivity was calculated as 96.7% [16].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

Yavuz Çelikdemir,

Akbal

2024

Journal of Polytechnic

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Cerebral aneurysms are an important disease that threatens human life. Rupture of these aneurysms causes hemorrhages in the cerebral arteries. Computed Tomography Angiography (CTA) is widely used in the clinical diagnosis of cerebral aneurysms. Interpretation errors by radiologists in examining these Computed Tomography (CT) images are vital for patients. Based on this importance, deep learning-based studies aim to help keep these errors to a minimum. For this purpose, CTA images were used to detect cerebral aneurysms in this study. For CTA image analysis, deep learning methodology was preferred through Convolutional Neural Network (CNN). The validation accuracy of the training obtained as a result of deep learningg has a high rate of validation with 99.54% accuracy, 100% sensitivity, 98.89% specificity and 100% precision. As a training dataset, it yielded 127 true positives and 1 false positive for patient images with aneurysm, 89 true negatives and 0 false negative for images of patients with non-aneurysms. In this trained network, results were obtained with a high accuracy of 86.6% on 75 CTA images for external test. Regional dimension analysis was also performed for an image with an aneurysm detected in the test process.

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“…Since cerebral arteries transport blood to deeper parts of the brain, ultrasound pictures of the middle cerebral artery have significant clinical significance [ 4 ]. Mei et al [ 16 ] provide a similar study in which they evaluated TCD images captured from the middle cerebral artery. The CNN VGG16 model was utilized to classify the images into the stenosis and non-stenosis groups.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

et al. 2023

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Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator’s skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.

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Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network

Cited by 7 publications

References 28 publications

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

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