Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing

Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.

Recent advances and clinical implications of intravascular imaging

Coronary artery disease (CAD) remains a major contributor to the global mortality rate. Accurate and detailed evaluation of atherosclerotic plaque characteristics is essential for effective risk assessment and treatment planning. Although conventional coronary angiography excels at quantifying luminal stenosis, information on plaque composition and structure remains limited. Recent advances in intravascular imaging (IVI) have bridged this gap by enabling high-resolution visualization of the vessel wall and plaque morphology, thereby enhancing treatment strategies and facilitating comprehensive risk stratification. Among the principal IVI modalities, intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) provide distinct benefits. IVUS accurately measures vessel diameter and plaque burden, offering critical guidance for managing complex lesions and left main artery disease. The extremely high spatial resolution of OCT allows precise identification of high-risk plaque features, such as thin fibrous caps. NIRS complements these techniques by quantitatively assessing lipid components within plaques, making it particularly useful in predicting future cardiovascular events. In this review, we summarize the latest evidence on applying IVI modalities to the evaluation and treatment of CAD. We focus on the assessment of plaque morphology, identification of high-risk lesions, and the role of IVI-guided percutaneous coronary intervention (PCI). The continued development of hybrid imaging systems and artificial intelligence-based image analysis may produce more precise and safer PCI approaches. Consequently, IVI is poised to become indispensable in managing CAD, paving the way for more personalized treatment strategies tailored to the specific lesion characteristics of each patient.

Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance

Artificial intelligence (AI) has profoundly influenced the field of cardiovascular interventions and coronary artery procedures in particular. AI has enhanced diagnostic accuracy in coronary artery disease through advanced invasive and noninvasive imaging modalities, facilitating more precise diagnosis and personalized interventional strategies. AI integration in coronary interventions has streamlined diagnostic and procedural workflows, improved procedural accuracy, increased clinician efficiency, and enhanced patient safety and outcomes. Despite its potential, AI still faces significant challenges, including concerns regarding algorithmic biases, lack of transparency in AI-driven decision making, and ethical challenges. This review explores the latest advancements of AI applications in coronary artery interventions, focusing on preprocedural planning and real-time procedural guidance. It also addresses the major limitations and obstacles that hinder the widespread clinical adoption of AI technologies in this field.

Artificial Intelligence in Intravascular Imaging for Percutaneous Coronary Interventions: A New Era of Precision

Intravascular imaging (IVI), including intravascular ultrasound and optical coherence tomography, play a crucial role in guiding percutaneous coronary intervention by providing detailed visualization of coronary anatomy and plaque morphology. Despite substantial evidence supporting IVI use, its adoption in clinical practice remains limited for multiple reasons including limited operator experience and a lack of confidence in image interpretation. The emergence of artificial intelligence presents a promising solution to these challenges by enhancing procedural efficiency and precision, thereby potentially increasing both IVI adoption and procedural optimization. This manuscript discusses the current applications, challenges, and future directions of artificial intelligence in IVI for percutaneous coronary intervention.

Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions

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.

AIMS

The primary endpoint was the agreement in stent size selection, within 0.25 mm, of AVVIGO ALA automatic segmentation of Class I lesions against the gold-standard measurement by an independent CL analysis and against an expert IC, (H. H.). The secondary endpoint was to assess the relative differences between AVVIGO ALA and CL, AVVIGO ALA and IC, and CL and IC, in vessel and lumen areas.

METHODS

Patients with complex coronary lesions, including left main bifurcation, long, and severely calcified lesions, were retrospectively analyzed using IVUS with ALA. Stent size selection and area measurements by ALA were compared against a CL and IC using established sizing methods.

RESULTS

In 48 patients, ALA demonstrated high agreement with CL (92%-100%) and IC (91%-98.5%) in stent size selection across lesion subtypes using recommended sizing methods. Lumen-based sizing achieved higher agreement than vessel-based sizing, particularly in calcified lesions (100% vs. 87%). The variability in relative difference in measurements between ALA and CL was greater than IC and CL in distal vessel and lesion vessel areas. The relative difference in measurements between ALA and IC was greater in vessel-based sizing compared to lumen-based sizing in the distal reference marker.

CONCLUSION

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.

The Role of Intravascular Ultrasound in the Evaluation and Treatment of Free-Floating Stent Struts Following Inadequate Ostial Circumflex Stenting: A Case Report

INTRODUCTION

Excessive stent strut protrusion in the distal left main (LM) from either the left anterior descending (LAD) or circumflex (Cx) artery following inadequate ostial stenting may complicate any later procedure involving the left coronary artery. In such case scenarios, intravascular ultrasound (IVUS) guidance provides accurate assessment of the ostial stent position and may facilitate subsequent management strategies and treatment.

CASE SUMMARY

We present a complex percutaneous coronary intervention (PCI) of LM bifurcation in a 49-year-old man following inadequate ostial Cx stenting that resulted in excessive stent protrusion in the distal LM segment, accompanied by a subsequent short 80-90% ostial LAD stenosis. Initially, IVUS was performed to confirm "floating struts" from a previous Cx ostial stenting and to ensure complete intraluminal placement of the wire within the stent leading to the Cx, precluding any side passage through the stent struts. Then, a second wire was inserted into the LAD through the most distal stent strut under live IVUS guidance. Further PCI was completed according to the principles of the double kissing mini-culotte technique. Final IVUS runs confirmed correct stent apposition and expansion in the LM, LAD and Cx segments.

CONCLUSIONS

In cases involving the treatment of "free-floating" struts in the distal LM artery, intravascular imaging is essential to ensure optimal PCI outcomes.

Innovations in Intracoronary Imaging: Present Clinical Practices and Future Outlooks

Engaging intracoronary imaging (IC) techniques such as intravascular ultrasound or optical coherence tomography enables the precise description of vessel architecture. These imaging modalities have well-established roles in providing guidance and optimizing percutaneous coronary intervention (PCI) outcomes. Furthermore, IC is increasingly recognized for its diagnostic capabilities, as it has the unique capacity to reveal vessel wall characteristics that may not be apparent through angiography alone. This manuscript thoroughly reviews the contemporary landscape of IC in clinical practice. Focused on current methodologies, the review explores the utility and advancements in IC techniques. Emphasizing their role in clarifying coronary pathophysiology, guiding PCI, and optimizing patient outcomes, the manuscript critically evaluates the strengths and limitations of each modality. Additionally, the integration of IC into routine clinical workflows and its impact on decision-making processes are discussed. By synthesizing the latest evidence, this review provides valuable insights for clinicians, researchers, and healthcare professionals involved in the dynamic field of interventional cardiology.