Can CTA-Based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy Is Insufficient?
The CTA-based ML model was able to more reliably predict unexpected poor functional outcome after successful EST and small FIV for patients with LVO AIS compared with standard clinical variables. ML models may identify a priori patients in whom EST-based LVO reperfusion alone is insufficient to improve clinical outcomes.
Authors
- Anjan Ballekere
- Luca Giancardo (ORCID: https://orcid.org/0000-0002-4862-2277)
- Yingjun Dong (ORCID: https://orcid.org/0000-0002-1935-1105)
- Sunil G. Sheth (ORCID: https://orcid.org/0000-0003-0399-4402)
- Rania Abdelkhaleq (ORCID: https://orcid.org/0000-0001-9619-6105)
- Jerome Jeevarajan (ORCID: https://orcid.org/0000-0002-6285-9350)
- Arash Niktabe
- Sergio Salazar Marioni
Institutions
- The University of Texas Health Science Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3174/ajnr.a8885
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00