Abstract 136: Reliability of an Automated, Machine‐Learning Algorithm for Volumetric Measurement of Patients with Chronic Subdural Hematoma
Hematoma volume in chronic subdural hematoma (CSDH) may predict neurological deterioration and need for surgical evacuation. Automated machine learning has shown promise in neuroimaging; however, the accuracy of automated subdural hematoma (SDH) volume measurement has not been compared to the Region‐of‐Interest (ROI) method.
Authors
- Adnan Iqbal Qureshi (ORCID: https://orcid.org/0000-0003-4962-540X)
- Stefano Genovese (ORCID: https://orcid.org/0000-0002-6085-437X)
- Peter Kan (ORCID: https://orcid.org/0000-0001-6649-4128)
- Farhan Siddiq (ORCID: https://orcid.org/0000-0001-8568-1543)
- Navpreet Bains (ORCID: https://orcid.org/0000-0003-2222-1798)
- Imtiaz Ali Bhatti
Institutions
- The University of Texas Medical Branch at Galveston (US)
- University of Missouri (US)
Publication Details
- Journal
- Stroke Vascular and Interventional Neurology
- Published
- 2024-11-01
- DOI
- https://doi.org/10.1161/svin.04.suppl_1.136
- Primary Topic
- Neurosurgical Procedures and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00