Intracranial hemorrhage (ICH) is bleeding within the cranium and occurs within the brain tissue, ventricles, and intracranial space. Hematoma expansion following an ICH has been related to increased mortality and morbidity inpatients. To detect ICH patients at risk, machine learning models can be used to predict whether or not hematoma expansion will occur. This study aims to assess the feasibility of machine learning prediction models using a radiomics approach. The highest sensitivity results indicated as 95% confidence intervals are 0.68 ± 0.004 and 0.72 ± 0.004, were achieved by support vector machine and logistic regression classifier models, respectively.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords