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
- brain injury
- computed tomography
- respiratory failure
- image quality
- dual energy
- chronic kidney disease
- newly diagnosed
- drug induced
- high resolution
- magnetic resonance imaging
- artificial intelligence
- peritoneal dialysis
- convolutional neural network
- aortic dissection
- prognostic factors
- machine learning
- positron emission tomography
- hepatitis b virus
- intensive care unit
- mass spectrometry
- acute respiratory distress syndrome
- mechanical ventilation
- risk assessment