Artificial intelligence empowered coronary artery imaging: A review.

Heart-related conditions remain the primary global driver of mortality, with blockages in heart vessels representing the most frequent pathology. While machine learning has transformed diagnostic visualization, comprehensive syntheses regarding heart vessel analysis remain scarce. This gap motivated a detailed examination of automated computational tools for cardiac imaging. Prior research has shown that automated systems can assist in complex vessel evaluation. However, no prior work had resolved the full spectrum of current algorithmic capabilities across diverse clinical modalities. That uncertainty drove the need for a systematic evaluation of existing literature. Investigators required a clear overview of how these digital tools perform in real-world clinical settings. This synthesis provides the necessary framework to understand the current state of automated heart vessel diagnostics.

The aim of this review is to provide a systematic and critical analysis of automated applications in heart vessel visualization. This study addresses the lack of focused literature regarding digital tools for coronary diagnostics. The authors seek to categorize these applications into three primary domains: measurement, functional assessment, and disease diagnosis. By organizing these areas, the researchers provide clarity on how computational models assist clinicians. The motivation stems from the rapid evolution of diagnostic technology and the need for a unified perspective. This work intends to inform both medical practitioners and technical developers about current capabilities. It also highlights the limitations that currently hinder widespread clinical adoption. Ultimately, the team strives to support the future translation of these advanced tools into daily medical practice.

Review approach involved a systematic search of Web of Science, Google Scholar, and MEDLINE databases. The team examined literature published between 2016 and 2025 to capture recent technological advancements. Initial screening identified 9,950 records for potential inclusion in the analysis. Researchers removed duplicates before performing title and abstract evaluations. They applied predefined criteria to ensure only high-quality, relevant studies remained for final assessment. Full-text eligibility checks confirmed the suitability of the remaining papers. This rigorous process yielded 90 studies for detailed synthesis. The methodology focused on evaluating performance across three distinct clinical domains and seven imaging platforms.

Key findings from the literature indicate that automated systems demonstrate varying levels of success across different diagnostic tasks. The analysis of 90 selected studies shows that performance metrics are highly dependent on the specific imaging modality. Researchers observed that current models excel in tasks like vessel segmentation and centerline extraction. Functional assessments, such as calculating wall shear stress, also benefit from these computational approaches. However, the data reveals significant inconsistencies in how different models handle diverse patient datasets. The review identifies that explainable models currently lag behind in clinical deployment compared to black-box systems. Computational requirements remain a substantial hurdle for many healthcare institutions seeking to integrate these tools. The findings emphasize that while progress is rapid, standardized validation protocols are still lacking.

The authors propose that automated diagnostic tools show significant promise for improving heart vessel evaluation. Synthesis and implications suggest that performance metrics fluctuate based on the specific imaging modality utilized. Researchers emphasize that clinical adoption depends on overcoming hurdles like data consistency and system interoperability. The review highlights that explainable models are necessary to foster trust among medical practitioners. Emerging techniques like federated learning may address privacy concerns while enhancing model robustness. Experts suggest that multimodal data integration could provide a more comprehensive view of patient health. Future efforts should prioritize validating these systems across diverse, multi-center datasets to ensure broad applicability. The team concludes that bridging the gap between computational development and clinical practice remains a primary objective.