Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan.
Sergio García-GarcíaSantiago CepedaDominik MüllerAlejandra MosteiroRamón TornéSilvia AgudoNatalia de la TorreIgnacio ArreseRosario SarabiaPublished in: Brain sciences (2023)
Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.
Keyphrases
- deep learning
- subarachnoid hemorrhage
- computed tomography
- dual energy
- convolutional neural network
- cerebral ischemia
- brain injury
- end stage renal disease
- artificial intelligence
- cardiovascular events
- image quality
- chronic kidney disease
- contrast enhanced
- newly diagnosed
- ejection fraction
- positron emission tomography
- prognostic factors
- risk factors
- magnetic resonance imaging
- machine learning
- working memory
- cardiovascular disease
- electronic health record
- coronary artery disease
- white matter
- magnetic resonance
- type diabetes
- multiple sclerosis
- patient reported outcomes
- blood brain barrier
- optical coherence tomography
- patient reported