Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.
Journal:
Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
PMID:
39981660
Abstract
BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but remains underutilized in part due to difficulties in image interpretation. The AVVIGO IVUS Automated Lesion Assessment (ALA) software, which uses machine learning (ML) for automatic segmentation, promises to simplify lesion assessment. This study evaluated the agreement in stent size selection between ALA, an independent core laboratory (CL), and an expert interventional cardiologist (IC) for complex lesions.
Authors
Keywords
Aged
Automation
Coronary Artery Disease
Coronary Vessels
Female
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Male
Middle Aged
Observer Variation
Percutaneous Coronary Intervention
Predictive Value of Tests
Prosthesis Design
Reproducibility of Results
Retrospective Studies
Severity of Illness Index
Stents
Treatment Outcome
Ultrasonography, Interventional