Deep Learning–Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time
With the assistance of the algorithm, readers' analyses were not only more accurate but also faster. Additionally, the overall ASPECTS evaluation exhibited greater consistency, fewer variabilities, and higher precision compared with the reference standard. This novel tool has the potential to enhance patient selection for appropriate treatment by enabling physicians to deliver accurate and timely diagnoses of acute ischemic stroke.
Authors
- Maxime Tassy (ORCID: https://orcid.org/0009-0008-7755-7356)
- Daniel Chow (ORCID: https://orcid.org/0000-0002-2359-7394)
- Angela Ayobi (ORCID: https://orcid.org/0000-0002-4351-6782)
- Kambiz Nael (ORCID: https://orcid.org/0000-0002-4194-9488)
- Yasmina Chaibi (ORCID: https://orcid.org/0000-0002-9734-172X)
- Peter Chang (ORCID: https://orcid.org/0000-0001-7645-7865)
- Sarah Quenet (ORCID: https://orcid.org/0009-0005-7192-1442)
- David Fussell (ORCID: https://orcid.org/0009-0003-0833-9148)
- Christophe Avare
- Sylvain Fogola
- Peter Shabe
- Adam Davis
Institutions
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- University of California, Los Angeles (US)
- Advantest (Singapore) (SG)
- University of California, Irvine (US)
- American Occupational Therapy Association (US)
- University of California, Irvine Medical Center (US)
- Amalgamated Sugar (United States) (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-09-10
- DOI
- https://doi.org/10.3174/ajnr.a8491
- Citations
- 6
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 2.08