Segmentation of the Hyperdense Artery Sign on Noncontrast CT in Ischemic Stroke Using Artificial Intelligence.

Background And Purpose: We developed and validated an automated hyperdense artery sign (HAS) segmentation algorithm for the distal internal carotid artery and middle cerebral artery on noncontrast brain computed tomography (NCCT) using a multicenter dataset with independent annotation performed by two experts.

Methods: For training and external validation, we included patients with ischemic stroke who underwent concurrent NCCT and CT angiography between May 2011 and December 2022 at six hospitals and one hospital, respectively. For clinical validation, nonoverlapping patients admitted within 24 hours of onset were consecutively included between December 2020 and April 2023 from six hospitals.

Segmentation of Leukoaraiosis on Noncontrast Head CT Using CT-MRI Paired Data Without Human Annotation.

Objective: Evaluating leukoaraiosis (LA) on CT is challenging due to its low contrast and similarity to parenchymal gliosis. We developed and validated a deep learning algorithm for LA segmentation using CT-MRIFLAIR paired data from a multicenter Korean registry and tested it in a US dataset.

Methods: We constructed a large multicenter dataset of CT-FLAIR MRI pairs.

Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.

We explored effects of (1) training with various sample sizes of multi-site vs. single-site training data, (2) cross-site domain adaptation, and (3) data sources and features on the performance of algorithms segmenting cerebral infarcts on Magnetic Resonance Imaging (MRI). We used 10,820 annotated diffusion-weighted images (DWIs) from 10 university hospitals.

Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance.

Purpose: Intracranial hemorrhage (ICH) requires urgent treatment, and accurate and timely diagnosis is essential for improving outcomes. This pivotal clinical trial aimed to validate a deep learning algorithm for ICH detection and assess its clinical utility through a reader performance test.

Methods: Retrospective CT scans from patients with and without ICH were collected from a tertiary hospital.

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

Background: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning algorithm in detecting unruptured intracranial aneurysms (UIAs) using time-of-flight (TOF) magnetic resonance angiography (MRA).

Persistent Beta-Blocker Therapy Reduces Long-Term Mortality in Patients With Acute Ischemic Stroke With Elevated Heart Rates.

Background: Elevated heart rate in patients with acute ischemic stroke is associated with increased risk of mortality. Beta-blocker therapy is well known to reduce heart rate.

Methods And Results: This study was a post hoc analysis of patients with acute ischemic stroke with maximum heart rates ≥100 bpm.

A multicenter validation and calibration of automated software package for detecting anterior circulation large vessel occlusion on CT angiography.

Purpose: To validate JLK-LVO, a software detecting large vessel occlusion (LVO) on computed tomography angiography (CTA), within a multicenter dataset.

Methods: From 2021 to 2023, we enrolled patients with ischemic stroke who underwent CTA within 24-hour of onset at six university hospitals for validation and calibration datasets and at another university hospital for an independent dataset for testing model calibration. The diagnostic performance was evaluated using area under the curve (AUC), sensitivity, and specificity across the entire study population and specifically in patients with isolated middle cerebral artery (MCA)-M2 occlusion.

Temporal Trends in Stroke Management and Outcomes Between 2011 and 2020 in South Korea: Results From a Nationwide Multicenter Registry.

Background: This study aims to evaluate temporal trends of advanced treatments and related clinical outcomes of ischemic stroke through a decade-long trend analysis, using data from a comprehensive, national, multicenter registry. We also seek to identify areas in need of improvement.

Methods And Results: This analysis involved patients with ischemic stroke or transient ischemic attack registered prospectively in the CRCS-K-NIH (Clinical Research Center for Stroke in Korea-National Institute of Health) registry between 2011 and 2020.

Comparative Effectiveness of Rosuvastatin Versus Atorvastatin in Acute Ischemic Stroke Treatment.

Background: Research specifically addressing the efficacy of rosuvastatin versus atorvastatin in patients with ischemic stroke is insufficient. Using a large stroke registry, we investigated whether 2 commonly used statins, rosuvastatin and atorvastatin, differ in their effectiveness in reducing the risk of vascular events in patients with acute ischemic stroke.

Methods: We analyzed data from a nationwide stroke registry in South Korea between January 2011 and April 2022.

A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.

Study Objectives: Undiagnosed or untreated moderate-to-severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and efficient approach for OSA screening, using simple facial photography and sleep questionnaires.

Optimal Cerebral Blood Flow Thresholds for Ischemic Core Estimation Using Computed Tomography Perfusion and Diffusion-Weighted Imaging.

Objective: Computed tomography perfusion (CTP) imaging is crucial in quantifying cerebral blood flow (CBF) and thereby making an endovascular treatment (EVT) after large vessel occlusion. However, CTP is prone to overestimating the ischemic core. We sought to delineate the optimal regional CBF (rCBF) thresholds of pre-EVT CTP.

Influence of prestroke glycemic status on outcomes by age in patients with acute ischemic stroke and diabetes mellitus.

Background: This study aimed to explore the association between admission HbA1c and the risk of 1-year vascular outcomes stratified by age group in patients with acute ischemic stroke (AIS) and diabetes mellitus (DM).

Methods: This study analyzed prospective multicenter data from patients with AIS and DM. Admission HbA1C were categorized as:≤6.

Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study.

Background: To evaluate the stand-alone efficacy and improvements in diagnostic accuracy of early-career physicians of the artificial intelligence (AI) software to detect large vessel occlusion (LVO) in CT angiography (CTA).

Methods: This multicenter study included 595 ischemic stroke patients from January 2021 to September 2023. Standard references and LVO locations were determined by consensus among three experts.

Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning.

Introduction: We developed and externally validated a fully automated algorithm using deep learning to detect large vessel occlusion (LVO) in computed tomography angiography (CTA).

Method: A total of 2,045 patients with acute ischemic stroke who underwent CTA were included in the development of our model. We validated the algorithm using two separate external datasets: one with 64 patients (external 1) and another with 313 patients (external 2), with ischemic stroke.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal.

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

Background: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO.

Methods: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals.

The usefulness of global longitudinal peak strain and left atrial volume index in predicting atrial fibrillation in patients with ischemic stroke.

Introduction: Detection of atrial fibrillation (AF) is crucial for preventing recurrence in patients with ischemic stroke. We aimed to examine whether the left atrial volume index (LAVI) and global longitudinal peak strain (GLPS) are associated with AF in patients with ischemic stroke.

Methods: We prospectively analyzed 678 consecutive patients with ischemic stroke.

Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles.

Background And Purpose: Multiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance.

Methods: We used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training.