Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%

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

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

Concurrent ischemic lesion age estimation and segmentation of CT brain using a transformer-based network

The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an event. These tasks are particularly challenging due to the subtle expression of acute ischemic lesions and the dynamic nature of their appearance. Automation efforts have not yet applied deep learning to estimate lesion age and treated these two tasks independently, so, have overlooked their inherent complementary relationship. To leverage this, we propose a novel end-to-end multi-task transformer-based network optimized for concurrent segmentation and age estimation of cerebral ischemic lesions. By utilizing gated positional self-attention and CT-specific data augmentation, the proposed method can capture long-range spatial dependencies while maintaining its ability to be trained from scratch under low-data regimes commonly found in medical imaging. Furthermore, to better combine multiple predictions, we incorporate uncertainty by utilizing quantile loss to facilitate estimating a probability density function of lesion age. The effectiveness of our model is then extensively evaluated on a clinical dataset consisting of 776 CT images from two medical centers. Experimental results demonstrate that our method obtains promising performance, with an area under the curve (AUC) of 0.933 for classifying lesion ages ≤4.5 hours compared to 0.858 using a conventional approach, and outperforms task-specific state-of-the-art algorithms

Methodology of a reevaluation of cardiovascular outcomes in the RECORD trial: study design and conduct

Background In 2010, after regulatory review of rosiglitazone licensing, the US Food and Drug Administration (FDA) requested a reevaluation of cardiovascular end points in the RECORD trial.<p></p> Methods Automated screening of the original clinical trial database and manual case report form review were performed to identify all potential cardiovascular and noncardiovascular deaths, and nonfatal myocardial infarction (MI) and stroke events. Search techniques were used to find participants lost to follow-up, and sites were queried for additional source documents. Suspected events underwent blinded adjudication using both original RECORD end point definitions and new FDA end point definitions, before analysis by the Duke Clinical Research Institute.<p></p> Results The reevaluation effort included an additional 328 person-years of follow-up. Automated screening identified 396 suspected deaths, 2,052 suspected MIs, and 468 suspected strokes. Manual review of documents by Duke Clinical Research Institute clinical events classification (CEC) coordinators identified an additional 31 suspected deaths, 49 suspected MIs, and 28 suspected strokes. There were 127 CEC queries issued requesting additional information on suspected deaths; 43 were closed with no site response, 61 were closed with a response that no additional data were available, and additional data were received for 23. Seventy CEC queries were issued requesting additional information for suspected MI and stroke events; 31 were closed with no site response, 20 were closed with a response that no additional data were available, and 19 resulted in additional data.<p></p> Conclusions Comprehensive procedures were used for rigorous event reascertainment and readjudication in a previously completed open-label, global clinical trial. These procedures used in this unique situation were consistent with other common approaches in the field, were enhanced to address the FDA concerns about the original RECORD trial results, and could be considered by clinical trialists designing event readjudication protocols for drug development programs that have been completed.<p></p&gt

Deep Learning Technique for Detecting and Analysing Ischemic Stroke Using MRI Images

The quantitative analysis of cerebral MRI images plays a pivotal role in stroke diagnosis and treatment. Deep learning, particularly CNNs, with their robust learning capabilities, offer an effective tool for lesion detection. To address the unique properties of stroke injuries and automate detection processes, we compiled a dataset of brain MRI images from various medical sources, representing patients affected by ischemic strokes. Different deep learning-based networks, including “Single Shot Multibox Detector (SSD)”, “Region-based CNN with ResNet101 (RCNN-ResNet101)”, “RCNN with VGG16 (RCNN- VGG16)”, and “YOLOV3”, were employed for automated lesion detection. The evaluation focused on achieving optimal precision in comparison to existing methods across Diffused Weight, Flair, and T1 modalities of MRI datasets. The developed technique involves extracting deep features during the encoding stage, followed by the minimization of features using fully connected layers. Significant handcrafted features, such as Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), were incorporated alongside deep features. The concatenation of these features was implemented to maximize the dimension of the feature vector. This concatenated vector was then used to train and test the performance of various classifiers. Binary classification was employed to categorize brain images into normal or stroke affected. Initially, SoftMax was used as the default classifier. The performance of each classifier was individually evaluated, and the best-performing classifier was selected to confirm the overall effectiveness of the proposed technique. This all-encompassing strategy not only leverages deep learning for automatic lesion detection but also integrates handcrafted features and diverse classifiers to improve the precision and dependability of stroke detection across various brain MRI image modalities

Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES Very poor outcome despite intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in about 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRT) in patients undergoing those therapies. MATERIALS AND METHODS Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The dataset was split into a training (N=1808, 80%) and internal validation (N=453, 20%) cohort. We used gradient boosted decision tree machine-learning models after k-NN imputation of 32 variables available at admission to predict FRT defined as modified Rankin-Scale (mRS) 5-6 at 3 months. We report feature importance, ability for discrimination, calibration and decision curve analysis. RESULTS 2261 patients with a median (IQR) age 75 years (64-83), 46% female, median NIHSS 9 (4-17), 34% IVT alone, 41% MT alone, 25% bridging were included. Overall 539 (24%) had FRT, more often in MT alone (34%) as compared to IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, CRP, creatinine), imaging biomarkers (white matter hyperintensities) and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (AUC 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004) and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8). CONCLUSIONS This FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. While it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance

CNN and Rf based Early Detection of Brain Stroke Using Bio-Electrical Signals

The brain is a vital component of the body that is in control of involuntary and voluntary movements such as walking,   memory, and vision. Nowadays, some of the most prevalent brain disorders include Alzheimer's disease, brain tumors, and epilepsy (paralysis or stroke).  As a result, stroke has become a significant global health concern, with high rates of mortality and disability. Importantly, approximately two-thirds of all strokes occur in developing countries, highlighting the significant burden of this condition in these regions. Therefore, emphasizing the timely detection and appropriate treatment of brain tumors is crucial. Given the high potential for mortality or severe disability associated with stroke disease, prioritizing active primary prevention and early identification of prognostic symptoms is of paramount importance. Ischemic stroke and hemorrhagic stroke are the two primary classifications for stroke diseases. Each type calls for specific emergency treatments, such as the administration of thrombolytics or coagulants, tailored to their respective underlying mechanisms. However, to effectively manage stroke, it is crucial to promptly identify the precursor symptoms in real-time, as they can vary among individuals. Timely professional treatment within the appropriate treatment window is essential and should be provided by a medical institution. In contrast, prior research has primarily centered around the formulation of acute treatment strategies or clinical guidelines subsequent to the occurrence of a stroke, rather than giving sufficient attention to the early identification of prognostic symptoms. Specifically, recent research has extensively utilized image analysis techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), as a primary approach for detecting and predicting prognostic symptoms in stroke patients. Traditional methodologies not only encounter difficulties in achieving early real-time diagnosis but also exhibit limitations in terms of prolonged testing duration and high testing costs. In this study, we introduce a novel system that employs machine learning techniques to predict and semantically interpret prognostic symptoms of stroke. Our approach utilizes real-time measurement of multi-modal bio-signals, namely electrocardiogram (ECG) and photoplethysmography (PPG), with a specific focus on the elderly population. To facilitate real-time prediction of stroke disease during walking, we have developed a stroke disease prediction system that incorporates a hybrid ensemble architecture. This architecture synergistically combines Convolutional Neural Network (CNN) and Random Forest (RF) models, enabling accurate and timely prognostication of stroke disease. The suggested method prioritises the convenience of use of bio-signal sensors for the elderly by collecting bio-signals from three electrodes placed on the index finger. These signals include ECG and PPG, and they are obtained while the participants walk. The CNN-RF model delivers satisfactory prediction accuracy when using raw ECG and PPG data. F1-Score, Sensitivity, Specificity, and Accuracy were the performance parameters used to evaluated the model's performance

Secular Trends in Ischemic Stroke Subtypes

Background: With an aging population and an increasing prevalence of therapy for atherosclerosis, it might be expected that stroke subtypes would be changing over time. Limited information exists on the ischemic stroke subtypes in adults in Canada. Methods: Patients referred to the Urgent TIA Clinic, in London, Ontario, between 2002-2012 were included. Secular trends were analyzed using Poisson regression with spline trend function. Ischemic stroke subtype classification was validated. Results: 3,445 consecutive patients (mean age + SD 64.8 + 14.9) were included. Cardioembolic strokes/TIAs increased from 21% in 2002 to 56% in 2012, whereas all other ischemic stroke subtypes decreased (p\u3c0.05). Separate analysis in men and women showed similar results. Conclusions: The decrease in atherosclerotic risk factors resulted in fewer strokes/TIAs caused by large artery atherosclerosis. On the contrary, cardioembolic strokes/TIAs have increased. This has important implications for more intensive investigation and treatment to reduce the risk of recurrent embolic stroke/TIA

Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain

Accurate determination of the onset time in acute ischemic stroke (AIS) patients helps to formulate more beneficial treatment plans and plays a vital role in the recovery of patients. Considering that the whole brain may contain some critical information, we combined the Radiomics features of infarct lesions and whole brain to improve the prediction accuracy. First, the radiomics features of infarct lesions and whole brain were separately calculated using apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences of AIS patients with clear onset time. Then, the least absolute shrinkage and selection operator (Lasso) was used to select features. Four experimental groups were generated according to combination strategies: Features in infarct lesions (IL), features in whole brain (WB), direct combination of them (IW) and Lasso selection again after direct combination (IWS), which were used to evaluate the predictive performance. The results of ten-fold cross-validation showed that IWS achieved the best AUC of 0.904, which improved by 13.5% compared with IL (0.769), by 18.7% compared with WB (0.717) and 4.2% compared with IW (0.862). In conclusion, combining infarct lesions and whole brain features from multiple sequences can further improve the accuracy of AIS onset time