Publications by authors named "Gi-Hun Park"
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.
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.
Article Synopsis
- - This study compared two software packages, RAPID and JLK-CTP, for estimating ischemic core and hypoperfused tissue volumes in stroke patients using computed tomography perfusion (CTP) scans.
- - Researchers analyzed data from 327 patients, finding that both software packages showed excellent agreement in estimating ischemic core volumes, particularly at a blood flow threshold of less than 30%.
- - Overall, JLK-CTP and RAPID proved to be reliable tools for assessing ischemic core volumes early after stroke onset, although there were some indications that they might slightly overestimate these volumes.
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.