Analysis of electrode arrangements for brain stroke diagnosis via electrical impedance tomography through numerical computational models

Objective.Rapid stroke-type classification is crucial for improved prognosis. However, current methods for classification are time-consuming, require expensive equipment, and can only be used in the hospital. One method that has demonstrated promise in a rapid, low-cost, non-invasive approach to stroke diagnosis is electrical impedance tomography (EIT). While EIT for stroke diagnosis has been the topic of several studies in recent years, to date, the impact of electrode placements and arrangements has rarely been analyzed or tested and only in limited scenarios. Optimizing the location and choice of electrodes can have the potential to improve performance and reduce hardware cost and complexity and, most importantly, diagnosis time.Approach.In this study, we analyzed the impact of electrodes in realistic numerical models by (1) investigating the effect of individual electrodes on the resulting simulated EIT boundary measurements and (2) testing the performance of different electrode arrangements using a machine learning classification model.Main results.We found that, as expected, the electrodes deemed most significant in detecting stroke depend on the location of the electrode relative to the stroke lesion, as well as the role of the electrode. Despite this dependence, there are notable electrodes used in the models that are consistently considered to be the most significant across the various stroke lesion locations and various head models. Moreover, we demonstrate that a reduction in the number of electrodes used for the EIT measurements is possible, given that the electrodes are approximately evenly distributed.Significance.In this way, electrode arrangement and location are important variables to consider when improving stroke diagnosis methods using EIT.

A deep learning wearable-based solution for continuous at-home monitoring of upper limb goal-directed movements

Introduction

Monitoring upper limb function is crucial for tracking progress, assessing treatment effectiveness, and identifying potential problems or complications. Hand goal-directed movements (GDMs) are a crucial aspect of daily life, reflecting planned motor commands with hand trajectories towards specific target locations. Previous studies have shown that GDM tasks can detect early changes in upper limb function in neurodegenerative diseases and can be used to track disease progression over time.

Methods

In this study, we used accelerometer data from stroke survivor participants and controls doing activities of daily living to develop an automated deep learning approach to detect GDMs. The model performance for detecting GDM or non-GDM from windowed data achieved an AUC of 0.9, accuracy 0.83, sensitivity 0.81, specificity 0.84 and F1 0.82.

Results

We further validated the utility of detecting GDM by extracting features from GDM periods and using these features to classify whether the measurements are collected from a stroke survivor or a control participant, and to predict the Fugl-Meyer assessment score from stroke survivors.

Discussion

This study presents a promising and reliable tool for monitoring upper limb function in a real-world setting, and assessing biomarkers related to upper limb health in neurological, neuromuscular and muscles disorders.

Frequency and Characteristics of Non-Neurological and Neurological Stroke Mimics in the Emergency Department

BACKGROUND

Stroke mimics are common in the emergency department (ED) and early detection is important to initiate appropriate treatment and withhold unnecessary procedures. We aimed to compare the frequency, clinical characteristics and predictors of non-neurological and neurological stroke mimics transferred to our ED for suspected stroke.

METHODS

This was a cross-sectional study of consecutive patients with suspected stroke transported to the ED of the University Hospital Essen between January 2017 and December 2021 by the city's Emergency Medical Service. We investigated patient characteristics, preclinical data, symptoms and final diagnoses in patients with non-neurological and neurological stroke mimics. Multinominal logistic regression analysis was performed to assess predictors of both etiologic groups.

RESULTS

Of 2167 patients with suspected stroke, 762 (35.2%) were diagnosed with a stroke mimic. Etiology was non-neurological in 369 (48.4%) and neurological in 393 (51.6%) cases. The most common diagnoses were seizures (23.2%) and infections (14.7%). Patients with non-neurological mimics were older (78.0 vs. 72.0 y, p < 0.001) and more likely to have chronic kidney disease (17.3% vs. 9.2%, p < 0.001) or heart failure (12.5% vs. 7.1%, p = 0.014). Prevalence of malignancy (8.7% vs. 13.7%, p = 0.031) and focal symptoms (38.8 vs. 57.3%, p < 0.001) was lower in this group. More than two-fifths required hospitalization (39.3 vs. 47.1%, p = 0.034). Adjusted multinominal logistic regression revealed chronic kidney and liver disease as independent positive predictors of stroke mimics regardless of etiology, while atrial fibrillation and hypertension were negative predictors in both groups. Prehospital vital signs were independently associated with non-neurological stroke mimics only, while age was exclusively associated with neurological mimics.

CONCLUSIONS

Up to half of stroke mimics in the neurological ED are of non-neurological origin. Preclinical identification is challenging and a high proportion requires hospitalization. Awareness of underlying etiologies and differences in clinical characteristics is important to provide optimal care.

The Positive Predictive Value of Pediatric Stroke Diagnoses in Administrative Data: A Retrospective Validation Study

Background

This retrospective cohort study aimed to examine the positive predictive value (PPV) of pediatric stroke diagnoses in the Danish National Registry of Patients (DNRP) and the impact of different stroke definitions on the PPV.

Methods

We included children registered with a stroke or stroke-related diagnosis in the DNRP between January 2017 through December 2020. Two assessors reviewed medical records and validated cases according to the American Heart and American Stroke Association (AHA/ASA) stroke definition. The level of interrater agreement was examined using kappa statistics. Validation by the AHA/ASA definition was compared with validation according to the definition in the International Classification of Disease 11th version (ICD-11) and the World Health Organization's definition.

Results

Stroke was confirmed in 120 of 309 included children, yielding an overall PPV of 0.39 (95% CI: 0.33-0.45). PPV varied across stroke subtypes from 0.83 (95% CI: 0.71-0.92) for ischemic stroke (AIS), 0.57 (95% CI: 0.37-0.76) for unspecified stroke, 0.42 (95% CI: 0.33-0.52) for intracerebral hemorrhage (ICH) to 0.31 (95% CI: 0.55-0.98) and 0.07 (95% CI: 0.01-0.22) for cerebral venous thrombosis and subarachnoid hemorrhage (SAH), respectively. Most non-confirmed ICH and SAH diagnoses were in children with traumatic intracranial hemorrhages (36 and 66% respectively). Among 70 confirmed AIS cases, 25 (36%) were identified in non-AIS code groups. PPV varied significantly across stroke definitions with the highest for the AHA/ASA definition (PPV = 0.39, 95% CI: 0.34-0.45) and the lowest for the WHO definition (PPV = 0.29, 95% CI: 0.24-0.34). Correspondingly, the incidence of pediatric AIS per 100.000 person-years changed from 1.5 for the AHA/ASA definition to 1.2 for ICD-11 and 1.0 for the WHO-definition. The overall interrater agreement was considered excellent (κ=0.85).

Conclusion

After validation, stroke was confirmed in only half of the children registered in the DNRP with a stroke-specific diagnosis. Non-validated administrative data should be used with caution in pediatric stroke research. Pediatric stroke incidence rates may vary markedly depending on which stroke definition is used.

Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos

According to the Korea Institute for Health and Social Affairs, in 2017, the elderly, aged 65 or older, had an average of 2.7 chronic diseases per person. The concern for the medical welfare of the elderly is increasing due to a low birth rate, an aging population, and the lack of medical personnel. The demand for services that take user age, cognitive capacity, and difficulty into account is rising. As a result, there is an increased demand for smart healthcare systems that can lower hospital admissions and offer patients individualized care. This has motivated us to develop an AI system that can easily screen and manage neurological diseases through videos. As neurological diseases can be diagnosed by visual analysis to some extent, in this study, we set out to estimate the possibility of a person having a neurological disease from videos. Among neurological diseases, we focus on stroke because it is a common condition in the elderly population and results in high mortality and morbidity worldwide. The proposed method consists of three steps: (1) transforming neurological examination videos into landmark data, (2) converting the landmark data into recurrence plots, and (3) estimating the possibility of a stroke using deep neural networks. Major features, such as the hand, face, pupil, and body movements of a person are extracted from test videos taken under several neurological examination protocols using deep-learning-based landmark extractors. Sequences of these landmark data are then converted into recurrence plots, which can be interpreted as images. These images can be fed into convolutional neural networks to classify stroke using feature-fusion techniques. A case study of the application of a disease screening test to assess the capability of the proposed method is presented.

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Utility of Transthoracic Echocardiography in Diagnostic Evaluation of Ischemic Stroke

Objective: Transthoracic echocardiography (TTE) is routinely performed as part of standard acute ischemic stroke (AIS) workup. However, the overall yield of TTE is unclear and many patients may undergo unnecessary investigations. This study aims to investigate the utility of TTE as part of AIS workup.

Methods: We collected data on consecutive patients with AIS who were admitted to our institution between 07/01/2016 and 09/30/2017. Patients were included based on neuroimaging-documented AIS, age >18 and neuroimaging studies. Primary endpoint was the proportion of cases in which TTE yielded relevant finding, defined as Atrial Septa Defect or Patent Foramen Ovale, left atrial enlargement, left ventricular thrombus or ejection fraction of <35%. Secondary endpoint was the proportion of patients who had a TTE-drive change in management.

Results: Among 548 AIS patients (median age 71 [59–81] years, 50% female), 482 (87%) underwent TTE. Clinically relevant findings were observed in 183 (38%) patients, leading to additional workup in 41 (8.5%). Further workup was associated with younger median age (58 [50–65] vs. 72 [62–81], p < 0.0001, and was less likely in suspected large vessel etiology (p = 0.02). Abnormal TTE lead to treatment change in 24 (5%) patients; 22/24 were started on anticoagulation. TTE results were less likely to influence treatment changes in older patients (71 [60–80] vs. 58 [49–69] years, p = 0.02) with known atrial fibrillation (p = 0.01).

Conclusion: Our findings suggest that despite widespread use, the overall yield of TTE in AIS is low. Stratifying patients according to their likelihood of benefitting from it will be important toward better resource utilization.

Machine Learning in Acute Ischemic Stroke Neuroimaging

Machine Learning (ML) through pattern recognition algorithms is currently becoming an essential aid for the diagnosis, treatment, and prediction of complications and patient outcomes in a number of neurological diseases. The evaluation and treatment of Acute Ischemic Stroke (AIS) have experienced a significant advancement over the past few years, increasingly requiring the use of neuroimaging for decision-making. In this review, we offer an insight into the recent developments and applications of ML in neuroimaging focusing on acute ischemic stroke.

Vertebral artery dissection in rheumatoid arthritis with cervical spine disease

A 59-year-old woman with long-standing active rheumatoid arthritis presented with posterior circulation ischemic stroke after vertebral dissection. She had severe multilevel degenerative changes of her cervical spine. She did not have classic stroke risk factors nor evidence of atherosclerotic disease or other systemic diseases. The most likely mechanism appears to be injury of the artery wall by an osteophyte, causing dissection that resulted in thrombosis and subsequent embolic strokes.

Clinical use of computed tomographic perfusion for the diagnosis and prediction of lesion growth in acute ischemic stroke

BACKGROUND

Computed tomography perfusion (CTP) mapping in research centers correlates well with diffusion-weighted imaging (DWI) lesions and may accurately differentiate the infarct core from ischemic penumbra. The value of CTP in real-world clinical practice has not been fully established. We investigated the yield of CTP-derived cerebral blood volume (CBV) and mean transient time (MTT) for the detection of cerebral ischemia and ischemic penumbra in a sample of acute ischemic stroke (AIS) patients.

METHODS

We studied 165 patients with initial clinical symptoms suggestive of AIS. All patients had an initial noncontrast head CT, CTP, CT angiogram (CTA), and follow-up magnetic resonance imaging (MRI) of the brain. The obtained perfusion images were used for image processing. CBV, MTT, and DWI lesion volumes were visually estimated and manually traced. Statistical analysis was conducted using R and SAS software.

RESULTS

All normal DWI sequences had normal CBV and MTT studies (N = 89). Seventy-three patients had acute DWI lesions. CBV was abnormal in 23.3% and MTT was abnormal in 42.5% of these patients. There was a high specificity (91.8%) but poor sensitivity (40.0%) for MTT maps predicting positive DWI. The Spearman correlation was significant between MTT and DWI lesions (ρ = 0.66; P > .0001) only for abnormal MTT and DWI lesions >0 cc. CBV lesions did not correlate with final DWI.

CONCLUSIONS

In real-world use, acute imaging with CTP did not predict stroke or DWI lesions with sufficient accuracy. Our findings argue against the use of CTP for screening AIS patients until real-world implementations match the accuracy reported from specialized research centers.