Gaby Abou Karam

Feb 5, 2024 | nature.com | Anh Tran |Tal Zeevi |Gaby Abou Karam |Kevin N. Sheth

AbstractHematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence.