Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans
A novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists' ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.
Authors
- Christian Booz (ORCID: https://orcid.org/0000-0002-1052-7389)
- Ina Koch (ORCID: https://orcid.org/0000-0002-3621-003X)
- Tommaso D’Angelo (ORCID: https://orcid.org/0000-0003-0004-6378)
- Silvio Mazziotti (ORCID: https://orcid.org/0000-0001-9866-569X)
- Leon David Gruenewald (ORCID: https://orcid.org/0000-0001-7959-5008)
- Alfredo Blandino (ORCID: https://orcid.org/0000-0002-8185-8636)
- İbrahim Yel (ORCID: https://orcid.org/0000-0002-3894-4782)
- Leona S. Alizadeh (ORCID: https://orcid.org/0000-0001-8141-7454)
- Giuseppe Mauro Bucolo (ORCID: https://orcid.org/0000-0002-2076-5532)
- Simon S. Martin (ORCID: https://orcid.org/0000-0003-3347-4520)
- Thomas J. Vogl (ORCID: https://orcid.org/0000-0002-4590-376X)
- Tarek Kamareddine
Institutions
- University of Messina (IT)
- Goethe University Frankfurt (DE)
- Erasmus MC (NL)
- University Hospital Frankfurt (DE)
- Bundeswehrzentralkrankenhaus Koblenz (DE)
- Erasmus University Rotterdam (NL)
Publication Details
- Journal
- La radiologia medica
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1007/s11547-024-01867-y
- Citations
- 9
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 3.32