Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion
Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.
Authors
- Stefanie Warnat‐Herresthal (ORCID: https://orcid.org/0000-0002-0890-5774)
- Taraneh Ebrahimi (ORCID: https://orcid.org/0000-0002-5471-4266)
- Christian Thielscher (ORCID: https://orcid.org/0000-0002-7109-4820)
- Gabor C. Petzold (ORCID: https://orcid.org/0000-0002-0145-8641)
- Felix J. Bode (ORCID: https://orcid.org/0000-0003-4498-186X)
- Louisa Nitsch (ORCID: https://orcid.org/0000-0002-5139-6848)
- Sebastian Stösser (ORCID: https://orcid.org/0000-0002-1182-705X)
- Hannah Asperger (ORCID: https://orcid.org/0000-0003-3253-7016)
- Nils Lehnen (ORCID: https://orcid.org/0000-0001-9506-1452)
- Joachim L. Schultze (ORCID: https://orcid.org/0000-0003-2812-9853)
- Johannes Weller (ORCID: https://orcid.org/0000-0001-5818-5392)
- Julius N. Meißner
- Franziska Dorn (ORCID: https://orcid.org/0000-0002-9287-9512)
- Omid Shirvani
- Ivan Savchuk
- Niklas von Danwitz
- Julia Layer
Institutions
- University of Bonn (DE)
- University Hospital Bonn (DE)
- German Center for Neurodegenerative Diseases (DE)
Publication Details
- Journal
- Annals of Clinical and Translational Neurology
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1002/acn3.52185
- Citations
- 3
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.16