Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.
David DreizinTina ChenYuanyuan LiangYuyin ZhouFabio PaesYan WangAlan L YuillePatrick RothKathryn ChampGuang LiAshley McLenithanJonathan J MorrisonPublished in: Abdominal radiology (New York) (2021)
Current CT imaging paradigms are coarse, subjective, and limited for predicting which BHIs are most likely to benefit from AE. LPDI, automated using deep learning methods, may improve objective personalized triage of BHI patients to angiography at the point of care.
Keyphrases
- deep learning
- computed tomography
- end stage renal disease
- artificial intelligence
- image quality
- newly diagnosed
- ejection fraction
- dual energy
- machine learning
- emergency department
- convolutional neural network
- high resolution
- optical coherence tomography
- magnetic resonance imaging
- chronic kidney disease
- prognostic factors
- magnetic resonance
- trauma patients
- peritoneal dialysis
- molecular dynamics
- positron emission tomography
- molecular dynamics simulations
- photodynamic therapy
- patient reported outcomes