Intra-arterial tenecteplase after successful endovascular recanalisation in patients with acute posterior circulation arterial occlusion (ATTENTION-IA): multicentre randomised controlled trial.

Objective: To assess whether intra-arterial tenecteplase administered after successful endovascular recanalisation improves outcomes in patients with acute arterial occlusion of the posterior circulation.

Design: Multicentre randomised controlled trial.

Setting: 31 hospitals in China, 24 January 2023 to 24 August 2023.

Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.

Background And Purpose: Robustness against input data perturbations is essential for deploying deep-learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep-learning models' prediction errors. Testing deep-learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness.

Defining the typical characteristics of orthostatic headache in patients with spontaneous intracranial hypotension.

Background: Orthostatic headache (OH) is a common feature of various conditions, including spontaneous intracranial hypotension (SIH), but no precise definition currently exists outlining the typical OH characteristics. This ambiguity risks misdiagnosis with unnecessary investigations and delay in institution of treatment. The present study aimed to carry out structured phenotyping of OH in patients with SIH with the aim of outlining its typical characteristics.

Global Challenges in the Access of Endovascular Treatment for Acute Ischemic Stroke (Global MT Access).

Background: Mechanical thrombectomy (MT) is the standard of care for eligible acute ischemic stroke (AIS) patients with large vessel occlusion (LVO) since 2015.

Aim: Our aim was to determine the key challenges for MT implementation and access worldwide.

Methods: We conducted an international online survey consisting of 37 questions, distributed through the World Stroke Organization network, and as invited by co-authors between December 2022 and March 2023.

A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence.

Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers with nnU-NETv2 convolutional network for segmentation of ICH and PHE on non-contrast head CTs.