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Ischemic Stroke

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Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.

Scientific reports
Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and...

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Nature communications
Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial n...

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

Stroke
BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about...

ReachingBot: An automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice.

Journal of neuroscience methods
BACKGROUND: The single pellet reaching and grasp (SPRG) task is a behavioural assay widely used to study motor learning, control and recovery after nervous system injury in animals. The manual training and assessment of the SPRG is labour intensive a...

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

Stroke
BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models u...

Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning.

Journal of biophotonics
For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In th...

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

NeuroImage. Clinical
Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of ...

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in ...

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

Clinical neuroradiology
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 measurem...