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
- cardiovascular disease
- deep learning
- cardiovascular events
- subarachnoid hemorrhage
- computed tomography
- dual energy
- convolutional neural network
- artificial intelligence
- brain injury
- end stage renal disease
- cerebral ischemia
- type diabetes
- image quality
- newly diagnosed
- ejection fraction
- contrast enhanced
- healthcare
- chronic kidney disease
- positron emission tomography
- peritoneal dialysis
- big data
- machine learning
- risk factors
- magnetic resonance imaging
- prognostic factors
- electronic health record
- white matter
- multiple sclerosis
- patient reported outcomes
- data analysis