Collateral circulation- Evolving from time window to tissue window

Agarwal, Ayush; Sharma, A.; Vishnu, VenugopalanY; Srivastava, MV Padma

doi:10.4103/aian.aian_413_22

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Collateral circulation is an alternative network vessels that carries blood to the same destination tissue [21]. It serves as an auxiliary vascular system and plays a crucial role in preventing cerebral ischemia when the primary vascular pathways are partially obstructed [22].…”

Section: A Collateral Circulation Scoringmentioning

confidence: 99%

Collateral Circulation Classification Based on Cone Beam Computed Tomography Images using ResNet18 Convolutional Neural Network

Ali,

Abdullah,

Saad

et al. 2023

IJACSA

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Collateral circulation is an arterial anastomotic channel that supply nutrient perfusion to areas of the brain. It happens when there is an existence of disruption of regular sources of flow due to an ischemic stroke. The most recent method, Cone Beam Computed Tomography (CBCT) neuroimaging is able to provide specific details regarding the extent and adequacy of collaterals. The current approaches for collateral circulation classification are based on manual observation and lead to inter and intra-rater inconsistency. This paper presented a 2-class automatic classification that is recently growing very fast in artificial intelligence disciplines. The two classes will differentiate between good and poor collateral circulation. A pre-trained convolutional neural network (CNN), namely ResNet18, has been used to learn features and train using 4368 CBCT images. Initially, the dataset is prepared, labeled and augmented. Then the images were transferred to be trained using the ResNet18 method with certain specifications. The algorithm performance was then evaluated using metrics in terms of accuracy, sensitivity, specificity, F1 score and precision on the CBCT images to classify collateral circulation accurately. The findings can automate collateral circulation classification to ease the limitations of standard clinical practice. It is a convincing method that supports neuroradiologists in assessing clinical scans and helps neuroradiologists in clinical decisions about stroke treatment.

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Section: A Collateral Circulation Scoringmentioning

confidence: 99%

Collateral Circulation Classification Based on Cone Beam Computed Tomography Images using ResNet18 Convolutional Neural Network

Ali,

Abdullah,

Saad

et al. 2023

IJACSA

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Development and validation of a SCORing systEm for pre-thrombectomy diagnosis of IntraCranial Atherosclerotic Disease (Score-ICAD)

A Tarek,

Damiani Monteiro,

Mohammaden

et al. 2024

J NeuroIntervent Surg

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BackgroundEarly identification of intracranial atherosclerotic disease (ICAD) may impact the management of patients undergoing mechanical thrombectomy (MT). We sought to develop and validate a scoring system for pre-thrombectomy diagnosis of ICAD in anterior circulation large vessel/distal medium vessel occlusion strokes (LVOs/DMVOs).MethodsRetrospective analysis of two prospectively maintained comprehensive stroke center databases including patients with anterior circulation occlusions spanning 2010–22 (development cohort) and 2018–22 (validation cohort). ICAD cases were matched for age and sex (1:1) to non-ICAD controls.ResultsOf 2870 MTs within the study period, 348 patients were included in the development cohort: 174 anterior circulation ICAD (6% of 2870 MTs) and 174 controls. Multivariable analysis β coefficients led to a 20 point scale: absence of atrial fibrillation (5); vascular risk factor burden (1) for each of hypertension, diabetes, smoking, and hyperlipidemia; multifocal single artery stenoses on CT angiography (3); absence of territorial cortical infarct (3); presence of borderzone infarct (3); or ipsilateral carotid siphon calcification (2). The validation cohort comprised 56 ICAD patients (4.1% of 1359 MTs): 56 controls. Area under the receiver operating characteristic curve was 0.88 (0.84–0.91) and 0.82 (0.73–0.89) in the development and validation cohorts, respectively. Calibration slope and intercept showed a good fit for the development cohort although with overestimated risk for the validation cohort. After intercept adjustment, the overestimation was corrected (intercept 0, 95% CI −0.5 to –0.5; slope 0.8, 95% CI 0.5 to 1.1). In the full cohort (n=414), ≥11 points showed the best performance for distinguishing ICAD from non-ICAD, with 0.71 (95% CI 0.65 to 0.78) sensitivity and 0.82 (95% CI 0.77 to 0.87) specificity, and 3.92 (95% CI 2.92 to 5.28) positive and 0.35 (95% CI 0.28 to 0.44) negative likelihood ratio. Scores ≥12 showed 90% specificity and 63% sensitivity.ConclusionThe proposed scoring system for preprocedural diagnosis of ICAD LVOs and DMVOs presented satisfactory discrimination and calibration based on clinical and non-invasive radiological data.

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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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Collateral circulation- Evolving from time window to tissue window

Cited by 3 publications

References 37 publications

Collateral Circulation Classification Based on Cone Beam Computed Tomography Images using ResNet18 Convolutional Neural Network

Collateral Circulation Classification Based on Cone Beam Computed Tomography Images using ResNet18 Convolutional Neural Network

Development and validation of a SCORing systEm for pre-thrombectomy diagnosis of IntraCranial Atherosclerotic Disease (Score-ICAD)

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

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