Work

Predicting hematoma expansion after intracerebral hemorrhage: a comparison of clinician prediction with deep learning radiomics models

Year: 2025

Type: article

Source: Neurocritical Care

DOI: 10.1007/s12028-025-02214-3

Authors: Boyang Yu, Kara R. Melmed, Jennifer Frontera, Weicheng Zhu, Haoxu Huang, Adnan I. Qureshi, Abigail Maggard, Michael Steinhof, Lindsey Kuohn, Arooshi Kumar, Elisa R. Berson, Anh T. Tran, Seyedmehdi Payabvash, Natasha Ironside, Benjamin Brush, Seena Dehkharghani, Narges Razavian, Rajesh Ranganath

Institutions: New York University, University of Missouri, Courant Institute of Mathematical Sciences, Rush University Medical Center, Yale University, University of Virginia

Language: English

Topic: Intracerebral and Subarachnoid Hemorrhage Research

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: National Science Foundation, Doris Duke Charitable Foundation, Alzheimer's Association, National Institute of Neurological Disorders and Stroke

Awards: G1443945989, G2600604402, Robust and Interpretable Data-Driven Early Detection of Alzheimer's, Career: Building Models that Avoid Spurious Correlations through Interpretability and Representation Learning

OpenAlex ID: W4407242071

FWCI: 4.282

Cites: 20

Cited by: 7

Related to: 10