Artificial Intelligence Applications in Stroke

The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation

OBJECTIVE

To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.

METHODS

A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children's Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly.

RESULTS

Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636-0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759-0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741-0.884).

CONCLUSION

We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.

Assessment of Deep Learning-Based Triage Application for Acute Ischemic Stroke on Brain MRI in the ER

RATIONALE AND OBJECTIVES

To assess a deep learning application (DLA) for acute ischemic stroke (AIS) detection on brain magnetic resonance imaging (MRI) in the emergency room (ER) and the effect of T2-weighted imaging (T2WI) on its performance.

MATERIALS AND METHODS

We retrospectively analyzed brain MRIs taken through the ER from March to October 2021 that included diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences. MRIs were processed by the DLA, and sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were evaluated, with three neuroradiologists establishing the gold standard for detection performance. In addition, we examined the impact of axial T2WI, when available, on the accuracy and processing time of DLA.

RESULTS

The study included 947 individuals (mean age ± standard deviation, 64 years ± 16; 461 men, 486 women), with 239 (25%) positive for AIS. The overall performance of DLA was as follows: sensitivity, 90%; specificity, 89%; accuracy, 89%; and AUROC, 0.95. The average processing time was 24 s. In the subgroup with T2WI, T2WI did not significantly impact MRI assessments but did result in longer processing times (35 s without T2WI compared to 48 s with T2WI, p < 0.001).

CONCLUSION

The DLA successfully identified AIS in the ER setting with an average processing time of 24 s. The absence of performance acquire with axial T2WI suggests that the DLA can diagnose AIS with just axial DWI and FLAIR sequences, potentially shortening the exam duration in the ER.

The new era of artificial intelligence in neuroradiology: current research and promising tools

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke

BACKGROUND

Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.

METHODS

We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models.

RESULTS

Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors.

CONCLUSIONS

PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection

Cerebrovascular accidents (CVAs) often occur suddenly and abruptly, leaving patients with long-lasting disabilities that place a huge emotional and economic burden on everyone involved. CVAs result when emboli or thrombi travel to the brain and impede blood flow; the subsequent lack of oxygen supply leads to ischemia and eventually tissue infarction. The most important factor determining the prognosis of CVA patients is time, specifically the time from the onset of disease to treatment. Artificial intelligence (AI)-assisted neuroimaging alleviates the time constraints of analysis faced using traditional diagnostic imaging modalities, thus shortening the time from diagnosis to treatment. Numerous recent studies support the increased accuracy and processing capabilities of AI-assisted imaging modalities. However, the learning curve is steep, and huge barriers still exist preventing a full-scale implementation of this technology. Thus, the potential for AI to revolutionize medicine and healthcare delivery demands attention. This paper aims to elucidate the progress of AI-powered imaging in CVA diagnosis while considering traditional imaging techniques and suggesting methods to overcome adoption barriers in the hope that AI-assisted neuroimaging will be considered normal practice in the near future. There are multiple modalities for AI neuroimaging, all of which require collecting sufficient data to establish inclusive, accurate, and uniform detection platforms. Future efforts must focus on developing methods for data harmonization and standardization. Furthermore, transparency in the explainability of these technologies needs to be established to facilitate trust between physicians and AI-powered technology. This necessitates considerable resources, both financial and expertise wise which are not available everywhere.

Contributions of Machine Learning in the Management of Stroke: A Bibliometric Analysis of the 50 Most Cited Articles

BACKGROUND

Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke.

METHODS

Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies.

RESULTS

The total number of citations to the top 50 articles were 2959 (range 35-243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively).

CONCLUSIONS

Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients.

Artificial intelligence applied in acute ischemic stroke: from child to elderly

This review will summarize artificial intelligence developments in acute ischemic stroke in recent years and forecasts for the future. Stroke is a major healthcare concern due to its effects on the patient's quality of life and its dependence on the timing of the identification as well as the treatment. In recent years, attention increased on the use of artificial intelligence (AI) systems to help categorize, prognosis, and to channel these patients toward the right therapeutic procedure. Machine learning (ML) and in particular deep learning (DL) systems using convoluted neural networks (CNN) are becoming increasingly popular. Various studies over the years evaluated the use of these methods of analysis and prediction in the assessment of stroke patients, and at the same time, several applications and software have been developed to support the neuroradiologists and the stroke team to improve patient outcomes.

Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data

MOTIVATION

Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to caregivers and patients. Deep learning techniques might improve the predictions by incorporating different data modalities. We present a multimodal approach to predict the functional status of acute ischemic stroke patients after their discharge based on tabular data and CT perfusion imaging.

METHODS

We conducted experiments on tabular, imaging, and multimodal deep learning architectures to predict dichotomized mRS scores 3 months after the event. The dataset was collected from a Dutch hospital and includes 98 CVA patients with a visible occlusion on their CT perfusion scan. Tabular data is based on the Dutch Acute Stroke Audit data, and imaging data consists of summed-up CT perfusion maps.

RESULTS

On the tabular data, TabNet outperformed our baselines with an AUC of 0.71, while ResNet-10 on the imaging data performed comparably with an AUC of 0.70. Our implementation of the multimodal DAFT architecture outperforms baselines as well as comparable studies by achieving an 0.75 AUC, and 0.80 F1 score. This was achieved with a final model of less than a hundred thousand optimizable parameters, and a dataset less than half the size of reference papers.

CONCLUSION

Overall, we demonstrate the feasibility of predicting the functional outcome for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.

Exercise Intervention for Alzheimer's Disease: Unraveling Neurobiological Mechanisms and Assessing Effects

Alzheimer's disease (AD) is a progressive neurodegenerative disease and a major cause of age-related dementia, characterized by cognitive dysfunction and memory impairment. The underlying causes include the accumulation of beta-amyloid protein (Aβ) in the brain, abnormal phosphorylation, and aggregation of tau protein within nerve cells, as well as neuronal damage and death. Currently, there is no cure for AD with drug therapy. Non-pharmacological interventions such as exercise have been widely used to treat AD, but the specific molecular and biological mechanisms are not well understood. In this narrative review, we integrate the biology of AD and summarize the knowledge of the molecular, neural, and physiological mechanisms underlying exercise-induced improvements in AD progression. We discuss various exercise interventions used in AD and show that exercise directly or indirectly affects the brain by regulating crosstalk mechanisms between peripheral organs and the brain, including "bone-brain crosstalk", "muscle-brain crosstalk", and "gut-brain crosstalk". We also summarize the potential role of artificial intelligence and neuroimaging technologies in exercise interventions for AD. We emphasize that moderate-intensity, regular, long-term exercise may improve the progression of Alzheimer's disease through various molecular and biological pathways, with multimodal exercise providing greater benefits. Through in-depth exploration of the molecular and biological mechanisms and effects of exercise interventions in improving AD progression, this review aims to contribute to the existing knowledge base and provide insights into new therapeutic strategies for managing AD.

Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging

PURPOSE

To propose standardized and feasible imaging protocols for constructing artificial intelligence (AI) database in acute stroke by assessing the current practice at tertiary hospitals in South Korea and reviewing evolving AI models.

MATERIALS AND METHODS

A nationwide survey on acute stroke imaging protocols was conducted using an electronic questionnaire sent to 43 registered tertiary hospitals between April and May 2021. Imaging protocols for endovascular thrombectomy (EVT) in the early and late time windows and during follow-up were assessed. Clinical applications of AI techniques in stroke imaging and required sequences for developing AI models were reviewed. Standardized and feasible imaging protocols for data curation in acute stroke were proposed.

RESULTS

There was considerable heterogeneity in the imaging protocols for EVT candidates in the early and late time windows and posterior circulation stroke. Computed tomography (CT)-based protocols were adopted by 70% (30/43), and acquisition of noncontrast CT, CT angiography and CT perfusion in a single session was most commonly performed (47%, 14/30) with the preference of multiphase (70%, 21/30) over single phase CT angiography. More hospitals performed magnetic resonance imaging (MRI)-based protocols or additional MRI sequences in a late time window and posterior circulation stroke. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) were most commonly performed MRI sequences with considerable variation in performing other MRI sequences. AI models for diagnostic purposes required noncontrast CT, CT angiography and DWI while FLAIR, dynamic susceptibility contrast perfusion, and T1-weighted imaging (T1WI) were additionally required for prognostic AI models.

CONCLUSION

Given considerable heterogeneity in acute stroke imaging protocols at tertiary hospitals in South Korea, standardized and feasible imaging protocols are required for constructing AI database in acute stroke. The essential sequences may be noncontrast CT, DWI, CT/MR angiography and CT/MR perfusion while FLAIR and T1WI may be additionally required.

Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke center

PURPOSE

To estimate the ability of a commercially available artificial intelligence (AI) tool to detect acute brain ischemia on Magnetic Resonance Imaging (MRI), compared to an experienced neuroradiologist.

METHODS

We retrospectively included 1030 patients with brain MRI, suspected of stroke from January 6th, 2020 to 1st of April 2022, based on these criteria: Age ≥ 18 years, symptoms within four weeks before the scan. The neuroradiologist reinterpreted the MRI scans and subclassified ischemic lesions for reference. We excluded scans with interpretation difficulties due to artifacts or missing sequences. Four MRI scanner models from the same vendor were used. The first 800 patients were included consecutively, remaining enriched for less frequent lesions. The index test was a CE-approved AI tool (Apollo version 2.1.1 by Cerebriu).

RESULTS

The final analysis cohort comprised 995 patients (mean age 69 years, 53 % female). A case-based analysis for detecting acute ischemic lesions showed a sensitivity of 89 % (95 % CI: 85 %-91 %) and specificity of 90 % (95 % CI: 87 %-92 %). We found no significant difference in sensitivity or specificity based on sex, age, or comorbidities. Specificity was reduced in cases with DWI artifacts. Multivariate analysis showed that increasing ischemic lesion size and fragmented lesions were independently associated with higher sensitivity, while non-acute lesion ages lowered sensitivity.

CONCLUSIONS

The AI tool exhibits high sensitivity and specificity in detecting acute ischemic lesions on MRI compared to an experienced neuroradiologist. While sensitivity depends on the ischemic lesions' characteristics, specificity depends on the image quality.

Deep Learning-based Assessment of Internal Carotid Artery Anatomy to Predict Difficult Intracranial Access in Endovascular Recanalization of Acute Ischemic Stroke

BACKGROUND

Endovascular thrombectomy (EVT) duration is an important predictor for neurological outcome. Recently it was shown that an angle of ≤ 90° of the internal carotid artery (ICA) is predictive for longer EVT duration. As manual angle measurement is not trivial and time-consuming, deep learning (DL) could help identifying difficult EVT cases in advance.

METHODS

We included 379 CT angiographies (CTA) of patients who underwent EVT between January 2016 and December 2020. Manual segmentation of 121 CTAs was performed for the aortic arch, common carotid artery (CCA) and ICA. These were used to train a nnUNet. The remaining 258 CTAs were segmented using the trained nnUNet with manual verification afterwards. Angles of left and right ICAs were measured resulting in two classes: acute angle ≤ 90° and > 90°. The segmentations together with angle measurements were used to train a convolutional neural network (CNN) determining the ICA angle. The performance was evaluated using Dice scores. The classification was evaluated using AUC and accuracy. Associations of ICA angle and procedural times was explored using median and Whitney‑U test.

RESULTS

Median EVT duration for cases with ICA angle > 90° was 48 min and with ≤ 90° was 64 min (p = 0.001). Segmentation evaluation showed Dice scores of 0.94 for the aorta and 0.86 for CCA/ICA, respectively. Evaluation of ICA angle determination resulted in an AUC of 0.92 and accuracy of 0.85.

CONCLUSION

The association between ICA angle and EVT duration could be verified and a DL-based method for semi-automatic assessment with the potential for full automation was developed. More anatomical features of interest could be examined in a similar fashion.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Objective

Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.

Methods

Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.

Results

A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.

Conclusion

ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Artificial Intelligence and the Evaluation and Treatment of Stroke

Stroke affects close to 800,000 Americans every year and is a major cause of disability and mortality. Prompt, accurate diagnosis and treatment of stroke is of critical importance in minimizing these deleterious effects. Recent advances in computer technology have allowed the development artificial intelligence technology that can be applied to the diagnosis, treatment and rehabilitation of victims of stroke.

A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges

Intracranial aneurysms (IAs) are highly prevalent in the population, and their rupture poses a significant risk of death or disability. However, the treatment of aneurysms, whether through interventional embolization or craniotomy clipping surgery, is not always safe and carries a certain proportion of morbidity and mortality. Therefore, early detection and prompt intervention of IAs with a high risk of rupture is of notable clinical significance. Moreover, accurately predicting aneurysms that are likely to remain stable can help avoid the risks and costs of over-intervention, which also has considerable social significance. Recent advances in artificial intelligence (AI) technology offer promising strategies to assist clinical trials. This review will discuss the state-of-the-art AI applications for assessing the rupture risk of IAs, with a focus on achievements, challenges, and potential opportunities.

Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

INTRODUCTION

Stroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there is an emerging need to improve the accuracy of the methods used to identify and characterise stroke in prehospital settings and emergency departments (EDs). This might be accomplished by developing computerised decision support systems (CDSSs) that are based on artificial intelligence (AI) and potential new data sources such as vital signs, biomarkers and image and video analysis. This scoping review aims to summarise literature on existing methods for early characterisation of stroke by using AI.

METHODS AND ANALYSIS

The review will be performed with respect to the Arksey and O'Malley's model. Peer-reviewed articles about AI-based CDSSs for the characterisation of stroke or new potential data sources for stroke CDSSs, published between January 1995 and April 2023 and written in English, will be included. Studies reporting methods that depend on mobile CT scanning or with no focus on prehospital or ED care will be excluded. Screening will be done in two steps: title and abstract screening followed by full-text screening. Two reviewers will perform the screening process independently, and a third reviewer will be involved in case of disagreement. Final decision will be made based on majority vote. Results will be reported using a descriptive summary and thematic analysis.

ETHICS AND DISSEMINATION

The methodology used in the protocol is based on information publicly available and does not need ethical approval. The results from the review will be submitted for publication in a peer-reviewed journal. The findings will be shared at relevant national and international conferences and meetings in the field of digital health and neurology.

Research advances in imaging markers for predicting hematoma expansion in intracerebral hemorrhage: a narrative review

Introduction

Stroke is a major global health concern and is ranked as the second leading cause of death worldwide, with the third highest incidence of disability. Intracerebral hemorrhage (ICH) is a devastating form of stroke that is responsible for a significant proportion of stroke-related morbidity and mortality worldwide. Hematoma expansion (HE), which occurs in up to one-third of ICH patients, is a strong predictor of poor prognosis and can be potentially preventable if high-risk patients are identified early. In this review, we provide a comprehensive summary of previous research in this area and highlight the potential use of imaging markers for future research studies.

Recent advances

Imaging markers have been developed in recent years to aid in the early detection of HE and guide clinical decision-making. These markers have been found to be effective in predicting HE in ICH patients and include specific manifestations on Computed Tomography (CT) and CT Angiography (CTA), such as the spot sign, leakage sign, spot-tail sign, island sign, satellite sign, iodine sign, blend sign, swirl sign, black hole sign, and hypodensities. The use of imaging markers holds great promise for improving the management and outcomes of ICH patients.

Conclusion

The management of ICH presents a significant challenge, and identifying high-risk patients for HE is crucial to improving outcomes. The use of imaging markers for HE prediction can aid in the rapid identification of such patients and may serve as potential targets for anti-HE therapies in the acute phase of ICH. Therefore, further research is needed to establish the reliability and validity of these markers in identifying high-risk patients and guiding appropriate treatment decisions.

A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study

BACKGROUND

The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities.

OBJECTIVE

This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction.

METHODS

We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute care hospitals in the state of Florida, merged with the SDoH data from the American Community Survey. A case-control design was adopted to construct stroke and stroke mimic cohorts. We compared the algorithm performance and feature importance measures of the ML models (ie, gradient boosting machine and random forest) with those of the logistic regression model based on 3 sets of predictors. To provide insights into the prediction and ultimately assist care providers in decision-making, we used TreeSHAP for tree-based ML models to explain the stroke prediction.

RESULTS

Our analysis included 143,203 hospital visits of unique patients, and it was confirmed based on the principal diagnosis at discharge that 73% (n=104,662) of these patients had a stroke. The approach proposed in this study has high sensitivity and is particularly effective at reducing the misdiagnosis of dangerous stroke chameleons (false-negative rate <4%). ML classifiers consistently outperformed the benchmark logistic regression in all 3 input combinations. We found significant consistency across the models in the features that explain their performance. The most important features are age, the number of chronic conditions on admission, and primary payer (eg, Medicare or private insurance). Although both the individual- and community-level SDoH features helped improve the predictive performance of the models, the inclusion of the individual-level SDoH features led to a much larger improvement (area under the receiver operating characteristic curve increased from 0.694 to 0.823) than the inclusion of the community-level SDoH features (area under the receiver operating characteristic curve increased from 0.823 to 0.829).

CONCLUSIONS

Using data widely available at the time of patients' hospital presentations, we developed a stroke prediction model with high sensitivity and reasonable specificity. The prediction algorithm uses variables that are routinely collected by providers and payers and might be useful in underresourced hospitals with limited availability of sensitive diagnostic tools or incomplete data-gathering capabilities.

Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients' medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.

Posterior Limb of Internal Capsule Infarct Predicts Functional Outcome in Acute Terminal Internal Carotid Artery Occlusion After Thrombectomy

PURPOSES

This study investigated the impact of posterior limb of internal capsule (PLIC) infarct on outcomes of acute internal carotid artery (ICA) occlusion after endovascular thrombectomy (EVT) and the diagnostic accuracy of pretreatment noncontrast computerized tomography (NCCT) and computerized tomography angiography (CTA) findings.

METHODS

Patients who underwent EVT for acute ICA occlusion between September 2014 and August 2020 were included in the study. The patients were dichotomized as PLIC infarct or spared. The risk factors for PLIC infarct were investigated, and the association between infarct patterns and clinical outcomes were assessed using logistic regression analysis. Pretreatment NCCT and CTA findings, including PLIC hypodensity, choroid plexus enhancement (CPE), and posterior cerebral artery (PCA) flow status, were calculated for diagnosis of PLIC infarct.

RESULTS

Among 72 patients, the mean age was 70.9 years, and the mean stroke scale was 19.4. PLIC infarct was identified in 15 patients (20.8%). PLIC infarct was associated with worse 90-day functional outcome (P = 0.01, shift test). Lack of CPE is the only independent predictor of PLIC infarct (odds ratio: 127.48, P = 0.001). Lack of CPE and impaired PCA flow produce greater diagnostic accuracy for PLIC infarct than does NCCT hypodensity (area under the receiver operating characteristics curve: 0.85 and 0.76, P = 0.0005 and 0.02, respectively).

CONCLUSIONS

In acute ICA occlusion, PLIC infarct is an independent risk factor for worse clinical outcome at 90 days. The lack of CPE was associated with PLIC infarct, and pretreatment CTA can be useful for early diagnosis.

A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images

BACKGROUND AND OBJECTIVES

This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network.

METHODS

The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information.

RESULTS

The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD).

CONCLUSIONS

Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.

Research and application advances in rehabilitation assessment of stroke

Stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

BACKGROUND

A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limitedly used in the cerebrovascular field even though the diseases can lead to catastrophic consequences.

OBJECTIVE

This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases.

METHODS

Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases.

RESULTS

For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. Both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion.

CONCLUSION

Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Ethical Considerations in Surgical Decompression for Stroke

Stroke is a major cause of morbidity and mortality. Neurosurgical decompression is often considered for the treatment of malignant infarcts and intraparenchymal hemorrhages, but this treatment can be frought with ethical dilemmas. In this article, the authors outline the primary principles of bioethics and their application to stroke care, provide an overview of key ethical issues and special situations in the neurosurgical management of stroke, and highlight methods to improve ethical decision-making for patients with stroke. Understanding these ethical principles is essential for stroke care teams to deliver appropriate, timely, and ethical care to patients with stroke.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Patient and procedure selection for mechanical thrombectomy: Toward personalized medicine and the role of artificial intelligence

Mechanical thrombectomy (MT) for ischemic stroke due to large vessel occlusion is standard of care. Evidence-based guidelines on eligibility for MT have been outlined and evidence to extend the treatment benefit to more patients, particularly those at the extreme ends of a stroke clinical severity spectrum, is currently awaited. As patient selection continues to be explored, there is growing focus on procedure selection including the tools and techniques of thrombectomy and associated outcomes. Artificial intelligence (AI) has been instrumental in the area of patient selection for MT with a role in diagnosis and delivery of acute stroke care. Machine learning algorithms have been developed to detect cerebral ischemia and early infarct core, presence of large vessel occlusion, and perfusion deficit in acute ischemic stroke. Several available deep learning AI applications provide ready visualization and interpretation of cervical and cerebral arteries. Further enhancement of AI techniques to potentially include automated vessel probe tools in suspected large vessel occlusions is proposed. Value of AI may be extended to assist in procedure selection including both the tools and technique of thrombectomy. Delivering personalized medicine is the wave of the future and tailoring the MT treatment to a stroke patient is in line with this trend.

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Artificial Intelligence-Based MRI in Diagnosis of Injury of Cranial Nerves of Premature Infant and Its Correlation with Inflammation of Placenta

The study focused on the effects of artificial intelligence algorithms in magnetic resonance imaging (MRI) for diagnosing cranial nerve inflammation of placenta and the correlation between cranial nerve injury with placental inflammation was explored. The subjects were selected from 132 premature infants in the hospital. According to the pathological examination of placenta, 81 cases with chorioamnionitis were taken as the experimental group and 51 cases without chorioamnionitis were taken as the control group. The incidence of cranial nerve injury in different groups of premature infants was analyzed by MRI diagnosis based on the principal component analysis (PCA) artificial intelligence algorithm, so as to analyze the correlation between cranial nerve injury and placental inflammation in premature infants. It was found that when the PCA artificial intelligence algorithm was incorporated into MRI examination of cranial nerve injury of premature infant, the A (accuracy), P (precision), R (recall), and F1 values under the PCA algorithm were 92%, 93.75%, 90%, and 92.87%, respectively. The A, P, R, and F1 of the control group were 54%, 54.1%, 52%, and 53.03%, respectively; there were statistically significant differences between the two groups, P < 0.05. As for the correlation of placental inflammation and cranial nerve injury, the positive detection rate of the experimental group was 53.09%, and the positive detection rate of the control group was 15.69%, and the difference was statistically significant, P < 0.05. In conclusion, the PCA artificial intelligence algorithm has high effectiveness and high accuracy in auxiliary diagnosis of premature brain nerve injury, and placental inflammation greatly increases the chance of premature infant suffering from brain nerve injury.

SARS-CoV-2 infection and acute ischemic stroke in Lombardy, Italy

OBJECTIVE

To characterize patients with acute ischemic stroke related to SARS-CoV-2 infection and assess the classification performance of clinical and laboratory parameters in predicting in-hospital outcome of these patients.

METHODS

In the setting of the STROKOVID study including patients with acute ischemic stroke consecutively admitted to the ten hub hospitals in Lombardy, Italy, between March 8 and April 30, 2020, we compared clinical features of patients with confirmed infection and non-infected patients by logistic regression models and survival analysis. Then, we trained and tested a random forest (RF) binary classifier for the prediction of in-hospital death among patients with COVID-19.

RESULTS

Among 1013 patients, 160 (15.8%) had SARS-CoV-2 infection. Male sex (OR 1.53; 95% CI 1.06-2.27) and atrial fibrillation (OR 1.60; 95% CI 1.05-2.43) were independently associated with COVID-19 status. Patients with COVID-19 had increased stroke severity at admission [median NIHSS score, 9 (25th to75th percentile, 13) vs 6 (25th to75th percentile, 9)] and increased risk of in-hospital death (38.1% deaths vs 7.2%; HR 3.30; 95% CI 2.17-5.02). The RF model based on six clinical and laboratory parameters exhibited high cross-validated classification accuracy (0.86) and precision (0.87), good recall (0.72) and F1-score (0.79) in predicting in-hospital death.

CONCLUSIONS

Ischemic strokes in COVID-19 patients have distinctive risk factor profile and etiology, increased clinical severity and higher in-hospital mortality rate compared to non-COVID-19 patients. A simple model based on clinical and routine laboratory parameters may be useful in identifying ischemic stroke patients with SARS-CoV-2 infection who are unlikely to survive the acute phase.

Machine learning in neuroimaging: from research to clinical practice

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life.

The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.

Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning

Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detection times between 15 min and 120 min. Motion data were collected using triaxial accelerometer bracelets worn on both arms for 24 h. Eighty-four stroke patients with unilateral arm motor impairment and 101 healthy subjects participated in the study. Accelerometer data were divided into data windows of different lengths and analyzed using multiple machine learning algorithms. The results show that all algorithms performed well in separating the two groups early enough to be clinically relevant, based on wrist-worn accelerometers. The two evaluated deep learning models, fully convolutional network and InceptionTime, performed better than the classical machine learning models with an AUC score between 0.947-0.957 on 15 min data windows and up to 0.993-0.994 on 120 min data windows. Window lengths longer than 90 min only marginally improved performance. The difference in performance between the deep learning models and the classical models was statistically significant according to a non-parametric Friedman test followed by a post-hoc Nemenyi test. Introduction of wearable stroke detection devices may dramatically increase the portion of stroke patients eligible for revascularization and shorten the time to treatment. Since the treatment effect is highly time-dependent, early stroke detection may dramatically improve stroke outcomes.

Ischemic Penumbra: A Personal View

The concept of the ischemic penumbra was defined over 40 years ago by Lindsay Symon and his group and is now an established principle of all acute ischemic stroke therapies. These reperfusion treatments rescue threatened, critically hypoperfused brain tissue and have been proven to improve clinical outcomes. We have been fortunate to have observed and played a small part in the penumbral story from its beginnings in the 1970s to its pivotal position today. Over this period, we have witnessed penumbral imaging evolve from positron emission tomography through to magnetic resonance imaging and now predominantly computed tomography perfusion, with the advent of automated imaging facilitating case selection for reperfusion therapies. We and others have conducted clinical trials using penumbral imaging to extend the time window for intravenous thrombolysis and select patients for thrombectomy. Together with the concept of fast- and slow-growing ischemic infarct patterns, this embeds the penumbral principle in everyday clinical management. The opportunity now exists to make penumbral imaging even more portable, affordable, and more widely available using mobile platforms, novel imaging techniques, digital linkage, and artificial intelligence.

Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models

Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI₀), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI_24h) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI_24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI₀ as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29-0.68]) than U-Net (0.48 [0.18-0.68]), Random Forests (0.51 [0.27-0.66]), and clinical thresholding method (0.45 [0.25-0.62]) (P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.

A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

PURPOSE

Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes.

MATERIALS AND METHODS

Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort.

RESULTS

Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant.

CONCLUSION

RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Direct angiographic intervention for acute ischemic stroke with large vessel occlusion

Background:Rapid and effective revascularization is the cornerstone of acute ischemic stroke treatment. Endovascular thrombectomy reperfusion has dramatically changed patients' outcomes with large vessel occlusions. Results of randomized controlled trials have shown that the benefit of this treatment is highly time-sensitive. The purpose of this review is to introduce a new approach to acute stroke treatment - direct angiographic intervention for selected patients. Methods & Results:We examined the issues related to the current guideline-based practice, emphasizing whether the benefit of using advanced multimodal imaging is worth the potential resultant disability generated by delayed reperfusion times. The published evidence supporting this novel concept was reviewed, its safety and feasibility discussed, and appropriate predictive tools for patient selection explored.Conclusion:This new approach may provide benefits for appropriately selected patients. Further prospective trials are needed to clarify the risk-versus-benefit of this approach.Abbreviations and Acronyms: AHA: American Heart AssociationAIS: acute ischemic strokeASA: American Stroke AssociationASPECTS: Alberta Stroke Program Early Computed Tomography ScoreCT: computed tomographyCTA: computed tomography angiographyCTP: computed tomography perfusionDMVO: distal, medium vessel occlusionDSA: digital subtraction angiographyED: Emergency DepartmentEVT: endovascular thrombectomyFD: flat detectorIA: intra-arterialIV: intravenousICH: intracerebral hemorrhageMR: magnetic resonanceMRA: magnetic resonance angiographyMSU: mobile stroke unitNIHSS: National Institutes of Health Stroke ScaleNCCT: non-contrast computed tomographyPLVO: proximal, large vessel occlusionPPV: positive predictive valuePWI: magnetic resonance perfusion-weighted imaging.

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

Leveraging artificial intelligence in ischemic stroke imaging

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.