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

Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review

Neurocardiology is an evolving field focusing on the interplay between the nervous system and cardiovascular system that can be used to describe and understand many pathologies. Acute ischemic stroke can be understood through this framework of an interconnected, reciprocal relationship such that ischemic stroke occurs secondary to cardiac pathology (the Heart-Brain axis), and cardiac injury secondary to various neurological disease processes (the Brain-Heart axis). The timely assessment, diagnosis, and subsequent management of cerebrovascular and cardiac diseases is an essential part of bettering patient outcomes and the progression of medicine. Artificial intelligence (AI) and machine learning (ML) are robust areas of research that can aid diagnostic accuracy and clinical decision making to better understand and manage the disease of neurocardiology. In this review, we identify some of the widely utilized and upcoming AI/ML algorithms for some of the most common cardiac sources of stroke, strokes of undetermined etiology, and cardiac disease secondary to stroke. We found numerous highly accurate and efficient AI/ML products that, when integrated, provided improved efficacy for disease prediction, identification, prognosis, and management within the sphere of stroke and neurocardiology. In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. Our review shows an evident interest and exciting new frontier for neurocardiology with artificial intelligence and machine learning.

Mediators of the causal associations between protein ratios and ischemic stroke: a two-step Mendelian randomization study

BACKGROUND

Proteomics has revealed links between plasma proteins and ischemic stroke (IS), but the relationship between protein ratios, IS, and the effects of blood cells and serum uric acid (SUA) is underexplored.

METHODS

Using two-sample Mendelian randomization (MR), we assessed causal relationships between 2,821 protein ratios, 91 blood phenotypes, SUA, and IS subtypes. FDR correction was applied specifically to protein ratio analyses to account for multiple comparisons in the primary MR step. Significant associations were further validated through co-localization analysis, which assessed shared genetic architecture between exposure and outcome loci. This analysis used GWAS data from MEGASTROKE, GISCOME, minimizing confounding bias and reverse causation. Additionally, the total effects of protein ratio levels on IS were decomposed into direct and indirect effects mediated through multiple pathways. Sensitivity analyses ensured robustness.

RESULTS

The CD34/ITGAV ratio exhibited distinct effects on stroke risk, showing 34.9% increased odds of LAS (OR=1.349, 95% CI=1.097-1.658) while demonstrating protective effects against IS outcome progression (OR=0.564, 95% CI=0.380-0.838). Bayesian co-localization analysis revealed complete genetic overlap (PPH4 = 1) for key protein ratio-stroke subtype pairs: AIS with TGFBR2/THBD ratio, LAS with LGALS8/VWF ratio, CES with BST2/CEACAM1 and CD209/CLEC4G ratios. In mediation pathways, neutrophil parameters accounted for 54.4% of the prognosis effect in the ABHD14B/STAMBP-IS association, whereas SUA mediated only 1.3% of the PODXL2/SDC1 ratio-IS relationship.

CONCLUSIONS

Our MR study combined with co-localization analysis identifies causal links between protein interactions and IS, highlighting potential targets to disrupt pathways connecting protein ratio changes to IS incidence and outcomes, offering promising intervention avenues.

Remote Organ Damage Induced by Stroke: Molecular Mechanisms and Comprehensive Interventions

Significance: Damage after stroke is not only limited to the brain but also often occurs in remote organs, including the heart, lung, liver, kidney, digestive tract, and spleen, which are frequently affected by complex pathophysiological changes. The organs in the human body are closely connected, and signals transmitted through various molecular substances could regulate the pathophysiological changes of remote organs. Recent Advances: The latest studies have shown that inflammatory response plays an important role in remote organ damage after stroke, and can aggravate remote organ damage by activating oxidative stress, sympathetic axis, and hypothalamic axis, and disturbing immunological homeostasis. Remote organ damage can also cause damage to the brain, aggravating inflammatory response and oxidative damage. Critical Issues: Therefore, an in-depth exploration of inflammatory and oxidative mechanisms and adopting corresponding comprehensive intervention strategies have become necessary to reduce damage to remote organs and promote brain protection. Future Directions: The comprehensive intervention strategy involves multifaceted treatment methods such as inflammation regulation, antioxidants, and neural stem cell differentiation. It provides a promising treatment alternative for the comprehensive recovery of stroke patients and an inspiration for future research and treatment. The various organs of the human body are interconnected at the molecular level. Only through comprehensive intervention at the molecular and organ levels can we save remote organ damage and protect the brain after stroke to the greatest extent. Antioxid. Redox Signal. 00, 000-000.

Automatic etiological classification of stroke thrombus digital photographs using a deep learning model

Background

Etiological classification of ischemic stroke is fundamental for secondary prevention, but frequently results in undetermined cause. We aimed to develop a Deep Learning (DL)-based model for automatic etiological classification of ischemic stroke using digital images of thrombi retrieved by mechanical thrombectomy.

Methods

Patients with large vessel occlusion stroke subjected to mechanical thrombectomy between April 2016 and January 2023 at La Fe University and Polytechnic Hospital in Valencia were included. Thrombus digital images were obtained and clinical characteristics, including TOAST etiological classification as reference standard, were retrieved. Statistical analysis was performed to compare clinical characteristics between atherothrombotic and cardioembolic strokes. A DL method was designed based on two deep neural networks for: (1) image segmentation and (2) image classification including clinical characteristics. The metrics used were DICE coefficient for the segmentation network, and accuracy, precision, sensitivity, specificity and area under the curve (AUC) for the predictions of the classification network.

Results

A total of 166 patients (mean age 69 [SD, 13], 67 female) were included. TOAST classification was: 31 atherothrombotic, 87 cardioembolic, and 48 cryptogenic. The segmentation network achieved an average DICE coefficient of 0.96 [SD, 0.13]. The optimal fused imaging and clinical classification network had a 0.968 accuracy [95% CI, 0.935-0.994], and AUC of 0.947 [95% CI, 0.870-1]. Cryptogenic thrombi were classified as cardioembolic (96%) or atherothrombotic (4%).

Conclusion

Two convolutional neural networks perform the automatic segmentation of thrombus images and, combined with selected clinical characteristics, their accurate and precise classification into atherothrombotic or cardioembolic etiology in patients with acute ischemic stroke.

The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint

Purpose

The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management.

Participants

All centres ensure accuracy and completeness of the data. Only patients who did not refuse use of their routine data collected during or after their hospital stay are included in the repository. Approvals or waivers are obtained from the responsible ethics committees before data exchange. A formal data transfer agreement (DTA) is signed by all contributing centres. The centres then share their data, and files are stored centrally on a safe server at the University Hospital Zurich. There, patient identifiers are removed and images are algorithmically de-faced. De-identified structured clinical data are connected to the imaging data by a new identifier. Data are made available to participating centres which have entered into a DTA for stroke research projects.

Repository setup

Initially, MAGIC is set to comprise initial and first follow-up imaging of 2,500 AIS patients. Clinical data consist of a comprehensive set of patient characteristics and routine prehospital metrics, treatment and laboratory variables.

Outlook

Our repository will support research by leveraging the entire range of routinely collected imaging and clinical data. This dataset reflects the current state of practice in stroke patient evaluation and management and will enable researchers to retrospectively study clinically relevant questions outside the scope of randomized controlled clinical trials. New centres are invited to join MAGIC if they meet the requirements outlined here. We aim to reach approximately 10,000 cases by 2026.

Biomarkers for Early Detection of Stroke: A Systematic Review

Stroke remains a leading cause of mortality and disability worldwide. Identifying reliable biomarkers for stroke diagnosis and risk prediction could significantly improve patient outcomes through earlier intervention and better risk management. The objective of this systematic review is to systematically review recent studies investigating biomarkers for stroke diagnosis and risk prediction and to synthesize the most promising findings. We conducted a systematic review of 10 studies published between 2008 and 2023 that examined various biomarkers in relation to stroke. Studies were evaluated for quality using a simplified version of the Mixed Methods Appraisal Tool. The reviewed studies investigated a diverse array of biomarkers, including lipids, inflammatory markers, hemodynamic markers, microRNAs, metabolites, and neurodegenerative markers. Key findings include the following: (1) non-traditional lipid markers such as triglycerides and non-high-density lipoprotein cholesterol may be more predictive of stroke risk than low-density lipoprotein; (2) inflammatory markers, particularly IL-6, showed strong associations with stroke risk; (3) hemodynamic markers like midregional proatrial natriuretic peptide (MRproANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) demonstrated potential in distinguishing stroke subtypes; (4) specific microRNAs (miR-125a-5p, miR-125b-5p, miR-143-3p) were upregulated in acute ischemic stroke; (5) metabolomic studies identified novel markers such as tetradecanedioate and hexadecanedioate associated with cardioembolic stroke; (6) neurodegenerative markers (total-tau, neurofilament light chain) were linked to increased stroke risk. This review highlights the potential of various biomarkers in improving stroke diagnosis and risk prediction. While individual markers show promise, a multi-marker approach combined with clinical variables appears most likely to yield clinically useful tools. Further large-scale validation studies are needed before these biomarkers can be implemented in routine clinical practice.

The most efficient machine learning algorithms in stroke prediction: A systematic review

Background and Aims

Stroke is one of the most common causes of death worldwide, leading to numerous complications and significantly diminishing the quality of life for those affected. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and compare their performance. The papers have published in period from 2019 to August 2023.

Methods

The authors conducted a systematic search in PubMed, Scopus, Web of Science, and IEEE using the keywords "Artificial Intelligence," "Predictive Modeling," "Machine Learning," "Stroke," and "Cerebrovascular Accident" from 2019 to August 2023.

Results

Twenty articles were included based on the inclusion criteria. The Random Forest (RF) algorithm was introduced as the best and most efficient stroke ML algorithm in 25% of the articles (n = 5). In addition, in other articles, Support Vector Machines (SVM), Stacking and XGBOOST, DSGD, COX& GBT, ANN, NB, and RXLM algorithms were introduced as the best and most efficient ML algorithms in stroke prediction.

Conclusion

This research has shown a rapid increase in using ML algorithms to predict stroke, with significant improvements in model accuracy in recent years. However, no model has reached 100% accuracy or is entirely error-free. Variations in algorithm efficiency and accuracy stem from differences in sample sizes, datasets, and data types. Further studies should focus on consistent datasets, sample sizes, and data types for more reliable outcomes.

Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study

BACKGROUND

The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within medical data, enabling the identification of factors associated with etiological classification. However, there is currently a lack of research utilizing ML algorithms for predicting AIS etiology.

OBJECTIVE

We aimed to use interpretable ML algorithms to develop AIS etiology prediction models, identify critical factors in etiology classification, and enhance existing clinical categorization.

METHODS

This study involved patients with the Third China National Stroke Registry (CNSR-III). Nine models, which included Natural Gradient Boosting (NGBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and logistic regression (LR), were employed to predict large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE) using an 80:20 randomly split training and test set. We designed an SFS-XGB with 10-fold cross-validation for feature selection. The primary evaluation metrics for the models included the area under the receiver operating characteristic curve (AUC) for discrimination and the Brier score (or calibration plots) for calibration.

RESULTS

A total of 5,213 patients were included, comprising 2,471 (47.4%) with LAA, 2,153 (41.3%) with SVO, and 589 (11.3%) with CE. In both LAA and SVO models, the AUC values of the ML models were significantly higher than that of the LR model (P < 0.001). The optimal model for predicting SVO (AUC [RF model] = 0.932) outperformed the optimal LAA model (AUC [NGB model] = 0.917) and the optimal CE model (AUC [LGBM model] = 0.846). Each model displayed relatively satisfactory calibration. Further analysis showed that the optimal CE model could identify potential CE patients in the undetermined etiology (SUE) group, accounting for 1,900 out of 4,156 (45.7%).

CONCLUSIONS

The ML algorithm effectively classified patients with LAA, SVO, and CE, demonstrating superior classification performance compared to the LR model. The optimal ML model can identify potential CE patients among SUE patients. These newly identified predictive factors may complement the existing etiological classification system, enabling clinicians to promptly categorize stroke patients' etiology and initiate optimal strategies for secondary prevention.

Artificial intelligence in ischemic stroke images: current applications and future directions

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Causal effect of serum 25 hydroxyvitamin D concentration on cardioembolic stroke: Evidence from two-sample Mendelian randomization

BACKGROUND AND AIMS

The putative association between serum 25-hydroxyvitamin D concentration [25(OH)D] and the risk of cardioembolic stroke (CES) has been examined in observational studies, which indicate controversial findings. We performed Mendelian randomization (MR) analysis to determine the causal relationship of serum 25(OH)D with the risk of CES.

METHODS AND RESULTS

The summary statistics dataset on the genetic variants related to 25(OH)D was used from the published GWAS of European descent participants in the UK Biobank, including 417,580 subjects, yielding 143 independent loci in 112 1-Mb regions. GWAS summary data of CES was obtained from GIGASTROKE Consortium, which included European individuals (10,804 cases, 1,234,808 controls). Our results unveiled a causal relationship between 25(OH)D and CES using IVW [OR = 0.82, 95% CI: 0.67-0.98, p = 0.037]. Horizontal pleiotropy was not seen [MR-Egger intercept = 0.001; p = 0.792], suggesting an absence of horizontal pleiotropy. Cochrane's Q [Q = 78.71, p-value = 0.924], Rucker's Q [Q = 78.64, p-value = 0.913], and I2 = 0.0% (95% CI: 0.0%, 24.6%) statistic suggested no heterogeneity. This result remained consistent using different MR methods and sensitivity analyses, including Maximum likelihood [OR = 0.82, 95%CI: 0.67-0.98, p-value = 0.036], Constrained maximum likelihood [OR = 0.76, 95%CI: 0.64-0.90, p-value = 0.002], Debiased inverse-variance weighted [OR = 0.82, 95%CI: 0.68-0.99, p-value = 0.002], MR-PRESSO [OR = 0.82, 95%CI 0.77-0.87, p-value = 0.022], RAPS [OR = 0.82, 95%CI 0.67-0.98, p-value = 0.038], MR-Lasso [OR = 0.82, 95%CI 0.68-0.99, p-value = 0.037].

CONCLUSION

Our MR analysis provides suggestive evidence that increased 25(OH)D levels may play a protective role in the development of cardioembolic stroke. Determining the role of 25(OH)D in stroke subtypes has important clinical and public health implications.

Carotid stenosis and cryptogenic stroke

OBJECTIVES

Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present.

METHODS

Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability.

RESULTS

Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy.

CONCLUSIONS

Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.

Molecular Pathways of Vulnerable Carotid Plaques at Risk of Ischemic Stroke: A Narrative Review

The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of ischemic stroke secondary to large-artery atherosclerosis. In macroscopic evaluation, vulnerable plaques are characterized by one or more of the following features: microcalcification; neovascularization; lipid-rich necrotic cores (LRNCs); intraplaque hemorrhage (IPH); thin fibrous caps; plaque surface ulceration; huge dimensions, suggesting stenosis; and plaque rupture. Recognizing these macroscopic characteristics is crucial for estimating the risk of cerebrovascular events, also in the case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that are scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talk governing plaque inflammation, rupture potential, and thrombogenicity. The current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehensive analysis of the current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional attributes that characterize vulnerable plaques.

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.

The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI

This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC. 473 AIS patients were classified into four categories: mechanical thrombectomy, conservative therapy, hemorrhage, and other diseases. The results showed that the montage image significantly outperformed pCM in terms of accuracy (montage image = 0.76 ± 0.01, pCM = 0.54 ± 0.05) and the area under the curve (AUC) (montage image = 0.94 ± 0.01, pCM = 0.76 ± 0.01). This study demonstrates the usefulness of the montage method and its potential for overcoming the limitations of pCM.

Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties-Stroke and Epilepsy in particular-clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist's daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

Hemiplegia in acute ischemic stroke: A comprehensive review of case studies and the role of intravenous thrombolysis and mechanical thrombectomy

Acute ischemic stroke is a significant health concern worldwide, often leading to long-term disability and decreased quality of life. Rapid and appropriate treatment is crucial for achieving optimal outcomes in these patients. Intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) are two commonly used interventions for acute ischemic stroke, but their effectiveness in improving neurological symptoms and functional outcomes in patients with hemiplegia remains uncertain. The aim of this work was to evaluate the impact of IVT and MT within a 4.5-h time frame on patients with acute ischemic stroke and hemiplegia. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Relevant studies that assessed the impact of IVT and MT within 4.5-h on hemiplegia in patients with an acute ischemic stroke were included. Data were extracted and analyzed to determine the overall effects of these interventions. Most included case reports indicate positive outcomes in terms of neurological symptom improvement and functional recovery in patients with hemiplegia after receiving IVT and MT within the specified time frame. However, the heterogeneity among the patients and the limited use of IVT due to contraindications posed challenges in determining the most effective treatment option. The findings from the included studies demonstrate that both interventions led to a decrease in National Institutes of Health Stroke Scale scores, indicating an improvement in neurological symptoms. The results highlight the beneficial effects of early thrombolytic interventions and MT on the neurological status and functional outcomes of patients with an acute ischemic stroke.

Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals

Background

Effective radiology diagnostic services are crucial for the timely and precise diagnosis and treatment of stroke, a medical emergency, in multidisciplinary hospitals. However, the efficiency of these services might be impeded by various logistical and operational challenges present in a multidisciplinary hospital setup.

Objective

This review endeavours to explore the ways for optimizing stroke management in multi-disciplinary hospitals, delving into its benefits, current challenges, and future prospects.

Methods

Electronic databases, namely PubMed, Scopus, and Web of Science, were utilized for this review. Studies that focus on the organizational and functional aspects of radiology diagnostic services in multidisciplinary hospitals for stroke patients were analysed.

Results

This review delves into a variety of strategies that could be harnessed to enhance radiology diagnostic services, thereby better-serving stroke patients in multidisciplinary hospital settings. It sheds light on the current hurdles in the optimization of stroke management, discussing them in detail. This article also explores the application and significance of Process Mapping in streamlining workflow for stroke management in hospitals, providing insights into its benefits, challenges, and future implications. Furthermore, the potential of Artificial Intelligence (AI) and Machine Learning (ML) in refining stroke management processes is also analysed and discussed.

Conclusion

The quest for optimizing the organization of radiology diagnostic services in multidisciplinary hospitals unveils a multi-pronged pathway. It beckons a harmonious blend of technological innovation, operational finesse, and multidisciplinary camaraderie. stepwise implementation of the identified optimization strategies, coupled with a continual assessment of their impact on patient care and operational efficiency, is recommended.

Digital health technologies and artificial intelligence in cardiovascular clinical trials: A landscape of the European space

The recent pandemic ushered in a marked surge in the adoption of digital health technologies (DHTs), necessitating remote approaches aiming to safeguard both patient and healthcare provider well-being. These technologies encompass an array of terms, including e-health, m-health, telemedicine, wearables, sensors, smartphone apps, digital therapeutics, virtual and augmented reality, and artificial intelligence (AI). Notably, some DHTs employed in critical healthcare decisions may transition into the realm of medical devices, subjecting them to more stringent regulatory scrutiny. Consequently, it is imperative to understand the validation processes of these technologies within clinical studies. Our study summarizes an extensive examination of clinical trials focusing on cardiovascular (CV) diseases and digital health (DH) interventions, with particular attention to those incorporating elements of AI. A dataset comprising 107 eligible trials, registered on clinicaltrials.gov and International Clinical Trials Registry Platform (ICTRP) databases until 19 June 2023, forms the basis of our investigation. We focused on clinical trials employing DHTs in the European context, revealing a diverse landscape of interventions. Devices constitute the predominant category (45.8%), followed by behavioral interventions (17.8%). Within the CV domain, trials predominantly span pivotal or confirmatory phases, with a notable presence of smaller feasibility and exploratory studies. Notably, a majority of trials exhibit randomized, parallel assignment designs. When analyzing the multifaceted landscape of trial outcomes, we identified various categories such as physiological and functional measures, diagnostic accuracy, CV events and mortality, patient outcomes, quality of life, treatment adherence and effectiveness, quality of hospital processes, and usability/feasibility measures. Furthermore, we delve into a subset of 15 studies employing AI and machine learning, describing various study design features, intended purposes and the validation strategies employed. In summary, we aimed to elucidate the diverse applications, study design features, and objectives of the evolving CV-related DHT clinical trials field.

[A case of ischemic stroke in a patient with probable CADASIL]

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a rare inherited disorder in which thickening of the walls of small and medium-sized blood vessels blocks blood flow to the brain. Diagnosis of CADASIL is based on clinical presentation, neuroimaging findings, and genetic predisposition. This disease is uncommon in children; typically, symptoms manifest in individuals between the ages of 20 and 40, though some may exhibit symptoms later in life. Currently, the diagnosis of CADASIL is of significant interest as there is no specific treatment targeting its etiopathogenesis. This article describes the case of a 51-year-old patient with CADASIL who was diagnosed with recurrent ischemic stroke. The patient has a history of multiple strokes: in 2019 (at age 47), 2020 (at age 48), and 2021 (at age 49). The consequences of these strokes include mild spastic right-sided hemiparesis, moderate complex motor aphasia, mild sensory aphasia, and progressive cognitive impairment. Suspicion of CADASIL was based on the patient's medical history, clinical presentation, and typical neuroimaging findings.

Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis

Background and aims

It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment.

Methods

To build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled.

Results

By utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%).

Conclusion

In recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records

Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.