ArticleJACC. Advances2023
Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing.
Article in JACC. Advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Artificial Intelligence-Assisted Intravascular Imaging in Percutaneous Coronary Intervention: Current Status and Advances.Reviews in cardiovascular medicine · 2026Review
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- Beyond CABG vs. PCI: Contemporary and Future Coronary Revascularisation from Historical Evolution to Artificial Intelligence, Robotics, and Hybrid Strategies.Journal of clinical medicine · 2026Article
- Long-Term Clinical Outcomes of Acute Myocardial Infarction Based on Institutional Experience With IVUS.Journal of the American Heart Association · 2026Article
- Applications of Artificial Intelligence in Chronic Total Occlusion Revascularization: From Present to Future-A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Coronary Intravascular Imaging: A Comprehensive Review of Techniques, Applications, and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial Intelligence in Drug-Coated Cardiovascular Devices: A Narrative Review.Reviews in cardiovascular medicine · 2025Review
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA.Diagnostics (Basel, Switzerland) · 2025Review
- Factors Contributing to Low Utilization of Intracoronary Imaging in Clinical Practice: A White Paper.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Lesion Preparation Complete - You May Now Deploy Your Stent.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Intravascular Imaging for Percutaneous Coronary Interventions: A New Era of Precision.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- The Role of Intravascular Ultrasound in the Evaluation and Treatment of Free-Floating Stent Struts Following Inadequate Ostial Circumflex Stenting: A Case Report.Medicina (Kaunas, Lithuania) · 2024Article
- Innovations in Intracoronary Imaging: Present Clinical Practices and Future Outlooks.Journal of clinical medicine · 2024Review
- Harnessing Artificial Intelligence for Intravascular Imaging: Is it Percutaneous Coronary Intervention Ready?JACC. Advances · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate intravascular ultrasound (IVUS) measurements are important in IVUS-guided percutaneous coronary intervention optimization by choosing the appropriate device size and confirming stent expansion. Objectives: The purpose of this study was to assess the accuracy of machine learning (ML) automatic segmentation of coronary artery vessel and lumen dimensions and balloon sizing. Methods: Using expert analysis as the gold standard, ML segmentation of 60 MHz IVUS images was developed using 8,076 IVUS cross-sectional images from 234 patients, which were randomly split into training (83%) and validation (17%) data sets. The performance of ML segmentation was then evaluated using an independent test data set (437 images from 92 patients). The endpoints were the agreement rate between ML vs experts' measurements for appropriate balloon size selection, and lumen and acute stent areas. Appropriate balloon size was determined by rounding down from the mean vessel diameter or rounding up from the mean lumen diameter to the next balloon size. The difference of lumen area ≥0.5 mm Results: ML model segmentation correlated well with experts' segmentation for training data set with a correlation coefficient of 0.992 and 0.993 for lumen and vessel areas, respectively. The agreement rate in lumen and acute stent areas was 85.5% and 97.0%, respectively. The agreement rate for appropriate balloon size selection was 70.6% by vessel diameter only and 92.4% by adding lumen diameter. Conclusions: ML model IVUS segmentation measurements were well-correlated with those of experts and selected an appropriate balloon size in more than 90% of images.
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