Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis
AJNR 46:33-40, Lee,J.,et al, 2025
Primary Central Nervous System Vasculitis
NEJM 391:1028-1037, Salvarani,C.,et al, 2024
Endovascular Therapy for Acute Stroke with a Large Ischemic Region
NEJM 386:doi.10.1056/NEJMoa2118191, Yoshimura, S.,et al, 2022
Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images
AJNR 42:838-844, Shin, I.,et al, 2021
The First Examination of Diagnostic Performance of Automated Measurement of the Callosal Angle in 1856 Elderly Patients and Volunteers Indicates that 12.4% of Exams Met the Criteria for Possible Normal Pressure Hydrocephalus
AJNR 42:1942-1948, Morzage, M.,et al, 2021
Machine Learning Approach to Identify Stroke Within 4.5 Hours
Stroke 51:860-866, Lee, H.,et al, 2020
Accuracy of a Machine Learning Muscle MRI - Based Tool for the Diagnosis of Muscular Dystrophies
Neurol 94:e1094-e1102, Verdu-Diaz, J.,et al, 2020
Artificial Intelligence Applications in Stroke
Stroke 51:2573-2579, Mouridsen, K.,et al, 2020
Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours
Neurol 90:e1570-e1577, Wouters, A.,et al, 2018
A Fatal Case of Undiagnosed Candida Meningitis-Role of Computer-assisted Diagnosis
Neurologist 23:138-140, Finelli, P.F., 2018