ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Systematic characterization and automated alignment of coronary tree geometries.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Simulation-based machine learning for real-time assessment of side-branch hemodynamics in coronary bifurcation lesions.The international journal of high performance computing applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Coronary artery disease (CAD) is the most common form of cardiovascular disease, characterized by gradual narrowing of the artery walls due to plaque buildup. Computational fluid dynamics (CFD) is a non-invasive approach often used to investigate how these anatomical changes perturb local hemodynamics and contribute to the pathological mechanism of progression. Therefore, the accuracy of coronary tree alignment and anatomical feature detection is key to understanding these hemodynamically mediated mechanisms. Despite advances, current methods face challenges, such as the need for manual selection of landmarks, often resulting in a semi-automated experience. This study aims to improve this by developing a fully automated system to detect 3D anatomical characteristics and align coronary tree geometries in large clinical datasets. Our proposed algorithm enables full automatic placement of the corresponding centerline points and alignment evaluation through similarity-based assessment of Jaccard index (intersection over union) in a cohort of 73 coronary geometries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.