Can shape analysis differentiate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient ischemic attack?

Safety and effectiveness of anticoagulation in the management of acute stroke and transient ischemic attack due to intracranial and extracranial non-occlusive thrombus

INTRODUCTION

The awareness of nonocclusive thrombus has increased with the increasing frequency of imaging methods used for acute ischemic stroke; however, the best treatment for nonocclusive thrombi is still unknown. In this study, we examined how anticoagulants affect supra-aortic artery nonocclusive thrombus and clinical outcomes.

MATERIALS AND METHODS

This study included 52 patients with transient ischemic attack or stroke who were diagnosed with nonocclusive thrombi on computed tomography angiography at admission. Patients were treated with anticoagulant treatment and grouped according to treatment modality (either unfractionated heparin or low molecular weight heparin) and treatment duration. Primary safety outcome was major bleeding defined as immediate and clnically significant hemorrhage. Anticoagulant treatment was continued until the thrombus was resolved as determined by consecutive weekly computed tomography angiography controls. After thrombus resolution, treatment was directed according to the underlying etiology. Antiaggregation treatment was the preferred treatment after thrombus resolution for patients with no observed etiology.

RESULTS

The affected internal carotid arteries were most frequently located in the cervical segment (48 %). Complete resolution was achieved within 2 weeks in 50 patients (96 %). The involved vasculature included the following: the extracranial carotid artery segments (n = 26, 50 %), intracranial ICA segments (n = 10, 19 %), basilar artery segments (n = 8, 15 %) and MCA segments (n = 7, 13 %). The most common underlying pathologies were atherosclerosis (n = 17), atrial fibrillation (n = 17), undetermined embolic stroke (n = 8), dissection (n = 7), and malignancy (n = 2). No symptomatic intra- or extracranial bleeding complications due to anticoagulant use were observed in any patient during the study period. A good functional outcome (modified Rankin scale score 0-2) was achieved in 49 patients (94 %) at 3 months. There was no significant difference between treatment type and duration in terms of reinfarction (p = 0.97 and p = 0.78, respectively).

CONCLUSION

Anticoagulant treatment is safe and effective in symptomatic patients with intracranial or extracranial artery nonocclusive thrombus, regardless of the anticoagulant type, thrombus location and size.

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

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.

Henry gas solubility optimization double machine learning classifier for neurosurgical patients

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients' data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients' outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients' data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

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.

Transfer Learning-Based Classification Comparison of Stroke

One type of brain disease that significantly harms people's lives and health is stroke. The diagnosis and management of strokes both heavily rely on the quantitative analysis of brain Magnetic Resonance (MR) images. The early diagnosis process is of great importance for the prevention of stroke cases. Stroke prediction is made possible by deep neural networks with the capacity for enormous data learning. Therefore, in thus study, several deep neural network models, including DenseNet121, ResNet50, Xception, MobileNet, VGG16, and EfficientNetB2 are proposed for transfer learning to classify MR images into two categories (stroke and non-stroke) in order to study the characteristics of the stroke lesions and achieve full intelligent automatic detection. The study dataset comprises of 1901 training images, 475 validation images, and 250 testing images. On the training and validation sets, data augmentation was used to increase the number of images to improve the models’ learning. The experimental results outperform all the state of arts that were used the same dataset. The overall accuracy of the best model is 98.8% and the same value for precision, recall, and f1-score using the EfficientNetB2 model for transfer learning.

Differentiating Carotid Free-Floating Thrombus From Atheromatous Plaque Using Intraluminal Filling Defect Length on CTA: A Validation Study

OBJECTIVE

To validate a previously proposed filling defect length threshold of >3.8 mm on CT angiography (CTA) to discriminate between free-floating thrombus (FFT) and plaque of atheroma.

METHODS

This was a prospective multicenter observational study of 100 participants presenting with TIA/stroke symptoms and a carotid intraluminal filling defect on initial CTA. Follow-up CTA was obtained within 1 week and at weeks 2 and 4 if the intraluminal filling defect was unchanged in length. Resolution or decreased length was diagnostic of FFT, whereas its static appearance after 4 weeks was indicative of plaque. Diagnostic accuracy of FFT length was assessed by receiver operating characteristic analysis.

RESULTS

Ninety-five participants (mean [SD] age 68 [13] years, 61 men, 83 participants with FFT, 12 participants with a plaque) were evaluated. The >3.8-mm threshold had a sensitivity of 88% (73 of 83) (95% confidence interval [CI] 78%-94%) and specificity of 83% (10 of 12) (95% CI 51%-97%) (area under the curve 0.91, p < 0.001) for the diagnosis of FFT. The optimal length threshold was >3.64 mm with a sensitivity of 89% (74 of 83) (95% CI 80%-95%) and specificity of 83% (10 of 12) (95% CI 51%-97%). Adjusted logistic regression showed that every 1-mm increase in intraluminal filling defect length is associated with an increase in odds of FFT of 4.6 (95% CI 1.9-11.1, p = 0.01).

CONCLUSION

CTA enables accurate differentiation of FFT vs plaque using craniocaudal length thresholds.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov Identifier: NCT02405845.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that in patients with TIA/stroke symptoms, the presence of CTA-identified filling defects of lengths >3.8 mm accurately discriminates FFT from atheromatous plaque.

The prediction of asymptomatic carotid atherosclerosis with electronic health records: a comparative study of six machine learning models

BACKGROUND

Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS.

METHODS

Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1).

RESULTS

Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR.

CONCLUSIONS

Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

Fighting healthcare rocketing costs with value-based medicine: the case of stroke management

Value-Based Medicine (VBM) is imposing itself as 'a new paradigm in healthcare management and medical practice.In this perspective paper, we discuss the role of VBM in dealing with the large productivity issue of the healthcare industry and examine some of the worldwide industrial and technological trends linked with VBM introduction. To clarify the points, we discuss examples of VBM management of stroke patients.In our conclusions, we support the idea of VBM as a strategic aid to manage rising costs in healthcare, and we explore the idea that VBM, by establishing value-generating networks among different healthcare stakeholders, can serve as the long sought-after redistributive mechanism that compensate patients for the industrial exploitation of their personal medical records.

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

Background and purpose

Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.

Methods

A systematic review of acute stroke diagnostic-focused AI studies from January 2014 to February 2019 in PubMed, Medline, and Embase using terms: ‘artificial intelligence’ or ‘machine learning or deep learning’ and ‘ischemic stroke’ or ‘large vessel occlusion’ was performed.

Results

Variations of AI, including ML methods of random forest learning (RFL) and convolutional neural networks (CNNs), are used to detect LVO strokes. Twenty studies were identified that use ML. Alberta Stroke Program Early CT Score (ASPECTS) commonly used RFL, while LVO detection typically used CNNs. Image feature detection had greater sensitivity with CNN than with RFL, 85% versus 68%. However, AI algorithm performance metrics use different standards, precluding ideal objective comparison. Four current software platforms incorporate ML: Brainomix (greatest validation of AI for ASPECTS, uses CNNs to automatically detect LVOs), General Electric, iSchemaView (largest number of perfusion study validations for thrombectomy), and Viz.ai (uses CNNs to automatically detect LVOs, then automatically activates emergency stroke treatment systems).

Conclusions

AI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.

Artificial intelligence in healthcare: past, present and future

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.

[A case of internal carotid thrombus associated with brain infarction]

A 49-year-old man was transferred to our hospital with chief complaint of global aphasia and weakness of right upper and lower limbs. Brain MRI showed ultra-acute cerebral infarction in left anterior cerebral artery and middle cerebral artery territory and MRA showed occlusion of A2 and M2. Although t-PA was administrated intravenously, symptoms didn't improve and giant internal carotid thrombus (size 6 × 7 × 17 mm) was recognized at left internal carotid artery by carotid ultrasonography. After started anticoagulant therapy, thrombus was miniaturized gradually and finally disappeared. Anticoagulant therapy is effective to internal carotid thrombus and carotid ultrasonography is useful to confirm the effectiveness. We suggest that clinicians should enforce anticoagulant therapy for the first choice to internal carotid thrombus.