- Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head
This study compared the accuracy of a stand-alone AI model with consensus neuroradiologists’ interpretations in detecting mass effect and vasogenic edema on CT of the head. The ability to identify these findings could assist the clinical workflow through prioritizing the interpretation of abnormal cases.
- An Optimized CT Protocol for Detecting Suspected Cauda Equina Syndrome: A Comparative Analysis with MRI
This study prospectively evaluated the diagnostic accuracy of an optimized CT lumbar spine protocol as a potential alternative to MRI in assessing suspected neural compression. CT accurately identified all cases requiring urgent decompression. CT had 97% sensitivity, specificity, and positive and negative predictive values in detecting disc pathology in patients presenting with symptoms suggestive of acute neural compression compared with MRI.
- Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings
Ultrafast MR imaging (2.1 minutes) reconstructed with machine-learning assisted framework demonstrated significant reduction in motion artifacts and 98.5% agreement with the reference MR protocol (10 minutes) on the main clinical diagnosis, though there were greater image noise and geometric distortion on the ultrafast protocol.
Welcome to the new AJNR, Updated Hall of Fame, and more. Read the full announcements.
AJNR is seeking candidates for the position of Associate Section Editor, AJNR Case Collection. Read the full announcement.