More articles from Artificial Intelligence
- AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms
This proof-of-concept investigation used open-source generative adversarial networks to create synthetic lumbar spine MRI STIR from T1 and T2 sequences. Evaluating radiologists found the synthetic volumes were of equal or better quality in 77% of test patients and demonstrated equivalent or decreased motion artifacts in 78% of test patients. The synthetic volumes had high positive predictive value (75%-100%) but lower sensitivity (0%-67%) for common pathologies. The results from this study are a promising first step toward expediting imaging protocols.
- 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.