Automated ASPECTS in Acute Ischmeic Stroke: A Comparative Analysis with CT Perfusion
AJNR 40:2033-2038, Sundaram, V.K.,et al, 2019
Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis
AJNR 46:33-40, Lee,J.,et al, 2025
AI in Neurology: Everything, Everywhere, all at One PRT 2:Speech, Sentience, Scruples, and Service
Ann Neurol 98:431-447, Rizzo, M., 2025
Gait Analysis in Neurologic Disorders, Methodology, Applications and Clinical Considerations
Neurol 105:e214154, Ali,F.,et al, 2025
Primary Central Nervous System Vasculitis
NEJM 391:1028-1037, Salvarani,C.,et al, 2024
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
NEjM 388:1201-1208,1220, Haug,C.H. & Drazen,J.M., 2023
Accuracy of a Generative Artificial Intelligence Model in a Complex Diagnostic Challenge
JAMA 330:78-79, Kanjee,Z.,et al, 2023
Improving Neurology Clinical Care with Natural Language Processing Tools
Neurol 101:1010-1018, Ge,W.,et al, 2023
Ethical Considerations in Surgical Decompression for Stroke
Stroke 53:2676-2682, Shlobin, N.A.,et al, 2022
External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke
Ann Neurol 92:943-957, Mair,G.,et al, 2022
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
Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy
Stroke 48:1416-1419, Labovitz, D.L.,et al, 2017