Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization
The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.
Authors
- Daniel Chow (ORCID: https://orcid.org/0000-0002-2359-7394)
- Angela Ayobi (ORCID: https://orcid.org/0000-0002-4351-6782)
- Mar Roca-Sogorb (ORCID: https://orcid.org/0000-0001-7482-1563)
- Brent D. Weinberg (ORCID: https://orcid.org/0000-0002-7992-1747)
- Yasmina Chaibi (ORCID: https://orcid.org/0000-0002-9734-172X)
- Peter Chang (ORCID: https://orcid.org/0000-0001-7645-7865)
- Martina Cotena (ORCID: https://orcid.org/0009-0009-2169-3161)
- Sarah Quenet (ORCID: https://orcid.org/0009-0005-7192-1442)
- Colin Zuchowski
- Jennifer E. Soun (ORCID: https://orcid.org/0000-0001-7385-8532)
- Jacqueline C. Junn
Institutions
- Emory University (US)
- University of California, Irvine (US)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-11-28
- DOI
- https://doi.org/10.3390/diagnostics14232689
- Citations
- 5
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01