AI-enhanced recognition of occlusions in acute coronary syndrome (AERO-ACS): a retrospective study
The AI model demonstrated higher sensitivity and similar specificity compared with traditional STEMI criteria, improving OMI detection while reducing false positives. These findings suggest potential benefits in triage accuracy and resource utilization, but further prospective validation is needed to determine its clinical impact.
Authors
- Esha Vaish
- Zara Khan (ORCID: https://orcid.org/0000-0002-7624-6379)
- Lorraine B. Robbins (ORCID: https://orcid.org/0000-0003-2914-3630)
- Luka Katic
- Sara Diaz
- Sean Pinney (ORCID: https://orcid.org/0000-0002-0268-7817)
- Serdar Farhan (ORCID: https://orcid.org/0000-0002-8237-1280)
- Nitin Barman (ORCID: https://orcid.org/0000-0002-0355-1268)
- James Choi (ORCID: https://orcid.org/0000-0002-3144-5817)
- Darren Kong (ORCID: https://orcid.org/0000-0001-5402-9800)
- Vincent Torelli (ORCID: https://orcid.org/0009-0000-3849-2088)
- A J Silverman
- Alex Nagourney
Institutions
- Lenox Hill Hospital (US)
- Mount Sinai Hospital (US)
- Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Coronary Artery Disease
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1097/mca.0000000000001555
- Citations
- 2
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 2.78