Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.
Hyochul LeeJunhyeok LeeJoon JangInpyeong HwangKyu Sung ChoiJung Hyun ParkJin Wook ChungSeung Hong ChoiPublished in: Neuroradiology (2024)
The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.
Keyphrases
- deep learning
- contrast enhanced
- liver failure
- end stage renal disease
- computed tomography
- magnetic resonance
- brain injury
- respiratory failure
- image quality
- chronic kidney disease
- ejection fraction
- dual energy
- newly diagnosed
- high resolution
- magnetic resonance imaging
- aortic dissection
- drug induced
- peritoneal dialysis
- convolutional neural network
- positron emission tomography
- hepatitis b virus
- optic nerve
- risk assessment
- acute respiratory distress syndrome
- human health
- patient reported
- mechanical ventilation