Automatic Stroke Screening on Mobile Application: Features of Gyroscope and Accelerometer for Arm Factor in FAST

This study focuses on automatic stroke-screening of the arm factor in the FAST (Face, Arm, Speech, and Time) stroke screening method. The study provides a methodology to collect data on specific arm movements, using signals from the gyroscope and accelerometer in mobile devices. Fifty-two subjects were enrolled in this study (20 stroke patients and 32 healthy subjects). Given in the instructions of the application, the patients were asked to perform two arm movements, Curl Up and Raise Up. The two exercises were classified into three parts: curl part, raise part, and stable part. Stroke patients were expected to experience difficulty in performing both exercises efficiently on the same arm. We proposed 20 handcrafted features from these three parts. Our study achieved an average accuracy of 61.7%-74.2% and an average area under the ROC curve (AUC) of 66.2%-81.5% from the combination of both exercises. Compared to the FAST method used by examiners in a previous study (Kapes et al., 2014) that showed with an accuracy of 69%-77% for every age group, our study showed promising results for early stroke identification, giving that our study is based only on the arm factor.

Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

BACKGROUND AND PURPOSE

Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps.

MATERIALS AND METHODS

Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm³).

RESULTS

An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001).

CONCLUSIONS

Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.

Severity of leukoaraiosis in large vessel atherosclerotic disease

BACKGROUND AND PURPOSE

The severity of white matter hyperintensity, or leukoaraiosis, is a marker of cerebrovascular disease. In stroke, WMH burden is strongly linked to lacunar infarction; however, impaired cerebral perfusion due to extracranial or intracranial atherosclerosis may also contribute to WMH burden. We sought to determine whether WMH burden is associated with extracranial or intracranial stenosis in patients with AIS.

MATERIALS AND METHODS

Patients with AIS with admission head/neck CTA and brain MR imaging were included in this analysis. "Extracranial stenosis" was defined as >50% stenosis in the extracranial ICA, and "intracranial," as >50% stenosis in either the middle, anterior, or posterior cerebral arteries on CTA, on either side. WMHV was determined by using a validated semiautomated protocol. Multiple regression was used to assess the relationship between WMHV and extracranial/intracranial atherosclerosis.

RESULTS

Of 201 subjects, 51 (25.4%) had extracranial and 63 (31.5%) had intracranial stenosis. Mean age was 62 ± 15 years; 36% were women. Mean WMHV was 12.87 cm(3) in the extracranial and 8.59 cm(3) in the intracranial stenosis groups. In univariate analysis, age (P < .0001), SBP and DBP (P = .004), and HTN (P = .0003) were associated with WMHV. Extracranial stenosis was associated with greater WMHV after adjustment for intracranial stenosis (P = .04). In multivariate analysis including extracranial stenosis, only age (P < .0001) and HTN (P = .03) demonstrated independent effects on WMHV.

CONCLUSIONS

In our cohort of patients with AIS, age and HTN were the strongest determinants of the WMHV severity. Future studies are warranted to unravel further association between WMHV and cerebral vessel atherosclerosis.