Accelerating Prediction of Malignant Cerebral Edema After Ischemic Stroke with Automated Image Analysis and Explainable Neural Networks.
Hossein Mohammadian ForoushaniAli HamzehlooAtul KumarYasheng ChenLaura HeitschAgnieszka SlowikDaniel StrbianJin-Moo LeeDaniel S MarcusRajat DharPublished in: Neurocritical care (2021)
An LSTM neural network incorporating volumetric data extracted from routine CT scans identified all cases of malignant cerebral edema by 24 h after stroke, with significantly fewer false positives than a fully connected neural network, regression model, and the validated EDEMA score. This preliminary work requires prospective validation but provides proof of principle that a deep learning framework could assist in selecting patients for surgery prior to deterioration.
Keyphrases
- neural network
- deep learning
- computed tomography
- end stage renal disease
- subarachnoid hemorrhage
- ejection fraction
- minimally invasive
- newly diagnosed
- machine learning
- chronic kidney disease
- prognostic factors
- coronary artery bypass
- peritoneal dialysis
- artificial intelligence
- magnetic resonance imaging
- electronic health record
- convolutional neural network
- magnetic resonance
- clinical practice
- positron emission tomography
- patient reported outcomes
- data analysis
- patient reported