Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions
AVVIGO ALA demonstrated high agreement in stent size selection compared to a CL and expert IC. ML's ability to automate IVUS analysis may improve operator efficiency, reduce radiation exposure, and enhance the adoption of intravascular imaging in routine practice. It remains to be seen if it will impact of adoption of IVUS to guide complex PCI.
Authors
- Ron Waksman (ORCID: https://orcid.org/0000-0002-4063-9226)
- Yirga Kahsay
- Pablo M. Rubio
- Beni Verma (ORCID: https://orcid.org/0000-0003-0970-2028)
- Abhishek Chaturvedi (ORCID: https://orcid.org/0000-0003-0241-2845)
- Jason Galo (ORCID: https://orcid.org/0000-0001-9036-7049)
- Kalyan R. Chitturi (ORCID: https://orcid.org/0000-0003-0602-2684)
- Hayder Hashim (ORCID: https://orcid.org/0000-0003-2194-5428)
- Brian C. Case (ORCID: https://orcid.org/0000-0001-5355-9594)
- Abdullah K. Al-Qaraghuli
- Cheng Zhang (ORCID: https://orcid.org/0009-0002-9357-6027)
- Héctor M. García‐García
- Gary S Mintz
Institutions
- MedStar Washington Hospital Center (US)
- Cardiovascular Research Foundation (US)
Publication Details
- Journal
- Catheterization and Cardiovascular Interventions
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1002/ccd.31458
- Citations
- 2
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.01