- Improved Glioma Grading Using Deep Convolutional Neural Networks
Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas.
- Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100
Machine learning-based feature selection can identify parameters with higher performance in outcome prediction.
- Imaging Parameters of the Ipsilateral Medial Geniculate Body May Predict Prognosis of Patients with Idiopathic Unilateral Sudden Sensorineural Hearing Loss on the Basis of Diffusion Spectrum Imaging
Diffusion spectrum imaging can detect abnormalities of white matter microstructure along the central auditory pathway in patients with unilateral idiopathic sudden sensorineural hearing loss. The generalized fractional anisotropy value of the ipsilateral medial geniculate body may help to predict recovery outcomes.
- Acute Ischemic Stroke or Epileptic Seizure? Yield of CT Perfusion in a “Code Stroke” Situation
CTP patterns helped to differentiate acute ischemic stroke from epileptic seizure in a “code stroke“ situation. The results indicate that a hyperperfusion pattern, especially if not restricted to a vascular territory, may suggest reconsideration of intravenous thrombolytic therapy.
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