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Gaby Abou Karam

As seen in: Nature, MDPI, AHA/ASA Journals, AJNR Podcasts
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Articles

Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT

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By Gaby Abou Karam, Kevin N. Sheth
| AJNR Podcasts
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Voxel-Wise Map of Intracerebral Hemorrhage Locations Associated With Worse Outcomes

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By Gaby Abou Karam, Seyedmehdi Payabvash
| AHA/ASA Journals
Information & Authors Published In History Received: 9 July 2024 Revision received: 11 December 2024 Accepted: 28 January 2025 Published online: 7 March 2025 Permissions Request permissions for this article. Keywords cerebral hemorrhage hematoma hemorrhagic stroke linear models location Subjects Disclosures Dr Qureshi reports compensation from Chiesi USA, Inc, for consultant services.
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Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan - npj Digital Medicine

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By Anh Tran, Tal Zeevi, Gaby Abou Karam, Kevin N. Sheth
| Nature Verified
Abstract Hematoma 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.
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