ArticleFrontiers in artificial intelligence2026
VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Coronary Artery Disease (CAD) remains a significant worldwide health problem, making accurate evaluation of disease severity essential for treatment planning. The SYNTAX score, derived from X-ray coronary angiography (XCA) images, plays a key role in guiding treatment decisions. However, manual scoring is time-consuming, subject and prone to inter-observer variability, necessitating the development of an objective and reliable system. We proposed VMamba-QAG-Net, a novel five-stage pipeline designed for SYNTAX score estimation directly from XCA images. Stage 1 performs binary vessel segmentation to resolve extreme class imbalance, using a preprocessing of Bilateral Filtering, CLAHE, and Unsharp Masking to enhance vessel boundaries, an EfficientSS2D block with parallel five direction visual mamba scanning and Quantum Attention Gate (QAG) to preserve long-range vascular connectivity. Stage 2, executes multi-class anatomical labelling into 27 distinct coronary artery segments using a four-channel guided input, supported by a Vessel Prototype Memory Bank with metric learning and a centreline-weighted skeleton loss for thin distal structures. Stage 3 and 4 extract ordered vessel centrelines via skeletonization, calculates percentage diameter stenosis through perpendicular ray casting, and detects grade stenotic lesions, using Aquila Optimiser. Stage 5 combines the stenosis information to calculate the SYNTAX score and support treatment decisions in revascularisation. Our proposed model was evaluated on the ARCADE dataset and attained a Dice coefficient of 0.9070 and accuracy of 0.9841 for binary segmentation. For 27-class coronary artery segmentation achieved a Dice coefficient of 0.7512, accuracy of 0.9514, and present class Dice of 0.8214. These results demonstrate the effectiveness of the proposed framework and its potential to support cardiologists in clinical decision-making.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.