Acute Ischemic Stroke Management

Latest papers

• MedGate-Fusion: Integrating First-Encounter Semantic Narratives and Physiological Biomarkers for Prospective Stroke Risk Stratification

  Hemn Khdr, Mohammad Noaeen, Karim Keshavjee, Aziz Guergachi, Zahra Shakeri · 23 September 2026

  Prospective stroke risk stratification in primary care is challenging because early risk signals are distributed across routine biomarkers and unstructured clinical narratives. We propose MedGate-Fusion, a multi-modal gated architecture that integrates transformer-based embeddings of first-encounter…

• Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework

  Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher, Stephanie Mangesius, Constantin Eisenschink, Philipp Deisl, Michael Knoflach, Astrid E. Grams, Elke R. Gizewski, Rainer Schubert · 22 September 2026

  Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes models to over-rely on dominant modalities and underutilize complementary information. While self-supervised pretraining and selective parameter free…

• Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation

  Linus Britt, Maximilian Nielsen, Susan Klapproth, Andre Kemmling, Michael H. Lev, Gabriel Broocks, Rene Werner, Thilo Sentker · 18 September 2026

  Objectives: Quantitative assessment of infarct hypodensity on non-contrast computed tomography (NCCT), including net water uptake (NWU), requires manual or semi-manual lesion delineation, often guided by CT perfusion or diffusion-weighted MRI, limiting clinical applicability. Automated segmentation …

• LightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net

  Giorgi Nikvashvili, Hanxue Gu, Jie Bao, Kang Wang, Yang Yang · 10 September 2026

  Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation in ISLES'26. On a 146-case held-out cohort, flip te…

• Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets

  Atle Bj{\o}rnerud, Till Schellhorn, Thor H. Skatt{\o}r, Terje Nome, Jon Andr\'e Ottesen, Anne Hege Aamodt, Bradley J MacIntosh · 27 August 2026

  Objective: Diffusion-weighted MRI (DWI-MRI) is the gold standard for visualizing and quantifying acute ischaemic stroke (AIS). Although deep learning methods can accurately segment AIS lesions, the optimal image inputs and model architecture remain uncertain. We evaluated whether accurate AIS lesion…

• Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation

  Dexter Wen Jie Teo, Kumaradevan Punithakumar · 26 August 2026

  Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is difficult because lesions are subtle and share intensity characteristics with cerebrospinal fluid. Standard deep learning architectures trained across mul…

• Ensemble of Convolutional Neural Networks for StrokePrediction: Towards Improved Diagnostic Accuracy

  Md Shahriar Sajid · 26 August 2026

  Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early diagnosis and preventive measures can greatly reduce life loss and disabilities. Recent advancements in deep learning have led to novel computer-aided di…

• StrokeGuard: A Multi-Agent Guided System for Prehospital Stroke Assessment

  Wentao Yang, Zhenye Xu, Ruoyi Li, Musen Zhang, Yao Guo · 26 August 2026

  Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremely narrow time window, thereby saving valuable time for subsequent treatment. In clinical practice, FAST-based scales are widely used for prehospital st…

• AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages

  Maunil Shah, Vaanathi Sundaresan · 21 August 2026

  Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning. Non-contrast CT (NCCT), the first-line imaging modality for diagnosing ischemic infarcts, exhibits subtle infarct contrast, making manual delineation slow and labor-intensi…

• X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA

  Maedeh Hafezi Moghadas, Hakim Baazaoui, Lukas Bastian Otto, Susanne Wegener, Björn Menze, Ezequiel De la Rosa · 20 August 2026

  Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights to guide secondary treatment strategies, neurorehabilitation planning, and retrospective stroke research. However, clinical LMC grading via the ASITN…

• Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI

  Julia Huang, Camila Gonzalez, Rydham Goyal, Aja Zou, Sasha Alexander, Michael Moseley, Moss Y. Zhao, Gary K. Steinberg · 18 August 2026

  For patients with Moyamoya disease, impaired cerebrovascular reserve (CVR) is an important hemodynamic criterion for recommending extracranial-to-intracranial bypass surgery. Standard CVR assessment in this cohort uses paired arterial spin labeling (ASL) perfusion MRI acquired before and after aceta…

• From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

  Esra Zihni, Katryna Cisek, Hamzah Ziadeh, Hendrik Knoche, Robert Mikulik, John D. Kelleher · 7 August 2026

  Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians' reasoning. Motivated by a clinician user study calling for clinical guideline-aligned cut-of…

• FSB-Net: Frequency-Spatial Boundary Network for Brain Stroke Lesion Segmentation in Non-Contrast CT

  Linke Fan, Xianglong Li, Huixin Huang, Kai Shu · 24 July 2026

  Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet remains difficult due to the low contrast between lesion and normal brain tissue, heterogeneous lesion morphology across ischemic and hemorrhagic subtype…

• Stroke Prediction using Clinical and Social Features in Machine Learning

  Aidan Chadha · 7 July 2026

  Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disabil…

• Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

  Oliver D\"urr, Lisa Herzog, Pascal B\"uhler, Susanne Wegener, Beate Sick · 17 June 2026

  Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions. In acute ischemic stroke, mechanical thrombectomy has been shown to be more effective on average than lysis in rand…

• Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks

  Junyong Cao, Hakim Baazaoui, Chinmay Prabhakar, Suprosanna Shit, Lukas Bastian Otto, Susanne Wegener, Bjoern Menze, Ezequiel de la Rosa · 16 June 2026

  Leptomeningeal collaterals (LMCs) are an important prognostic factor in acute ischemic stroke. Existing automated methods rely on CT angiography (CTA), but individual LMCs are often too small to be resolved on CTA, limiting these methods to coarse collateral scoring. Digital subtraction angiography …

• StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT

  Weiru Wang, Susanne G. H. Olthuis, Elizaveta Lavrova, Robert J. van Oostenbrugge, Charles B. L. M. Majoie, Wim H. van Zwam, Ruisheng Su · 4 June 2026

  Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating imaging-based assessmen…

• An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke

  Thijs Stessen (University of Amsterdam) · 2 June 2026

  The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulations can in theory inform operators to make better decisions regarding treatment approaches and device selection, but are too slow to do so in practice…

• Vision-Core Guided Contrastive Learning for Balanced Multi-modal Prognosis Prediction of Stroke

  Liren Chen, Lidong Sun, Mingyan Huang, Junzhe Tang, Yinghui Zhu, Guanjie Wang, Yiqing Xia, Ting Xiao · 15 May 2026

  Deep learning and multi-modal fusion have demonstrated transformative potential in medical diagnosis by integrating diverse data sources. However, accurate prognosis for ischemic stroke remains challenging due to limitations in existing multi-modal approaches. First, current methods are predominantl…

• A Boundary-Metric Evaluation Protocol for Whiteboard Stroke Segmentation Under Extreme Imbalance

  Nicholas Korcynski · 3 March 2026

  The binary segmentation of whiteboard strokes is hindered by extreme class imbalance, caused by stroke pixels that constitute only $1.79%$ of the image on average, and in addition, the thin-stroke subset averages $1.14% \pm 0.41%$ in the foreground. Standard region metrics (F1, IoU) can mask thin-st…

• Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder

  Adam Marcus, Paul Bentley, Daniel Rueckert · 3 March 2026

  Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimagi…

• Clinically-aligned ischemic stroke segmentation and ASPECTS scoring on NCCT imaging using a slice-gated loss on foundation representations

  Hiba Azeem, Behraj Khan, Tahir Qasim Syed · 2 March 2026

  Rapid infarct assessment on non-contrast CT (NCCT) is essential for acute ischemic stroke management. Most deep learning methods perform pixel-wise segmentation without modeling the structured anatomical reasoning underlying ASPECTS scoring, where basal ganglia (BG) and supraganglionic (SG) levels a…

• Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals

  Jiyeong Kim, Stephen P. Ma, Nirali Vora, Nicholas W. Larsen, Julia Adler-Milstein, Jonathan H. Chen, Selen Bozkurt, Abeed Sarker, Juhee Cho, Jindeok Joo, Natali Pageler, Fatima Rodriguez, Christopher Sharp, Eleni Linos · 27 February 2026

  Stroke affected millions annually, yet poor symptom recognition often delayed care-seeking. To address risk recognition gap, we developed a passive surveillance system for early stroke risk detection using patient-reported symptoms among individuals with diabetes. Constructing a symptom taxonomy gro…

• StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery

  Leo Thomas Ramos, Angel D. Sappa · 18 February 2026

  We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with two ConvNeXt encoders, whose features are fused through a lightweight convolutional decoder based on stacked 1D operations, including a bottleneck projection a…

• Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke

  Anjali K. Kapoor (Department of Neurosurgery, NYU Langone Health, New York, USA), Anton Alyakin (Department of Neurosurgery, NYU Langone Health, New York, USA, Global AI Frontier Lab, New York University, Brooklyn, USA, Department of Neurosurgery, Washington University in Saint Louis, Saint Louis, USA), Jin Vivian Lee (Department of Neurosurgery, NYU Langone Health, New York, USA, Global AI Frontier Lab, New York University, Brooklyn, USA, Department of Neurosurgery, Washington University in Saint Louis, Saint Louis, USA), Eunice Yang (Department of Neurosurgery, NYU Langone Health, New York, USA, Columbia University Vagelos College of Physicians and Surgeons, New York, USA), Annelene M. Schulze (Department of Neurosurgery, NYU Langone Health, New York, USA), Krithik Vishwanath (Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, USA), Jinseok Lee (Global AI Frontier Lab, New York University, Brooklyn, USA, Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea), Yindalon Aphinyanaphongs (Department of Population Health, NYU Langone Health, New York, USA, Division of Applied AI Technologies, NYU Langone Health, New York, USA), Howard Riina (Department of Neurosurgery, NYU Langone Health, New York, USA, Department of Radiology, NYU Langone Health, New York, USA), Jennifer A. Frontera (Department of Neurology, NYU Langone Health, New York, USA), Eric Karl Oermann (Department of Neurosurgery, NYU Langone Health, New York, USA, Global AI Frontier Lab, New York University, Brooklyn, USA, Division of Applied AI Technologies, NYU Langone Health, New York, USA, Center for Data Science, New York University, New York, USA) · 12 February 2026

  Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) prediction has relied primarily on structured variables (e.g., age, NIHSS) and conventional machine learning. The ability of l…