{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:10:43Z","timestamp":1790143843075,"version":"4.0.1"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/frai.2026.1903775","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:50:15Z","timestamp":1790142615000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography"],"prefix":"10.3389","volume":"9","author":[{"given":"G.","family":"Sunilkumar","sequence":"first","affiliation":[{"name":"School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Kumaresan","sequence":"additional","affiliation":[{"name":"Centre For Artificial Intelligence Research (C-FAIR) Vellore Institute of Technology","place":["Vellore, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1007\/s42235-023-00332-2","article-title":"Improved reptile search algorithm by salp swarm algorithm for medical image segmentation","volume":"20","author":"Abualigah","year":"2023","journal-title":"J. Bion. Eng."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3390\/jimaging10020030","article-title":"A CNN hyperparameters optimisation based on particle swarm optimisation for mammography breast cancer classification","volume":"10","author":"Aguerchi","year":"2024","journal-title":"J. Imaging"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e3273","DOI":"10.7717\/peerj-cs.3273","article-title":"Hyperparameter optimisation of convolutional neural networks using particle swarm optimisation for diabetic retinopathy detection","volume":"11","author":"Almansour","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.aej.2025.02.037","article-title":"AOA-guided hyperparameter refinement for precise medical image segmentation","volume":"120","author":"Balaha","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"130781","DOI":"10.1016\/j.neucom.2025.130781","article-title":"SFAG-DeepLabv3+: an automatic segmentation approach for coronary angiography images","volume":"650","author":"Chen","year":"2025","journal-title":"Neurocomputing"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1056\/NEJMoa1408758","article-title":"Fractional flow reserve\u2013guided PCI for stable coronary artery disease","volume":"371","author":"De Bruyne","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"16815","DOI":"10.1038\/s41598-025-99460-4","article-title":"A metaheuristic optimisation-based approach for accurate prediction and classification of knee osteoarthritis","volume":"15","author":"Diab","year":"2025","journal-title":"Sci. Rep."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/jimaging11070209","article-title":"The robust vessel segmentation and centreline extraction: one-stage deep learning approach","volume":"11","author":"Epifanov","year":"2025","journal-title":"J. Imaging"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"109172","DOI":"10.1016\/j.cmpb.2025.109172","article-title":"CoroSAM: adaptation of the segment anything model for interactive segmentation in coronary angiograms","volume":"274","author":"Ferrari","year":"2025","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s12880-022-00734-4","article-title":"Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features","volume":"22","author":"Gao","year":"2022","journal-title":"BMC Med. Imaging"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1007\/s10554-024-03069-z","article-title":"Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve","volume":"40","author":"Hatfaludi","year":"2024","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"751","DOI":"10.4103\/jmp.jmp_251_25","article-title":"Reducing noise and improving image contrast with a hybrid approach to enhance the quality of medical imaging","volume":"50","author":"Ismael","year":"2025","journal-title":"J. Med. Phys."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"957","DOI":"10.3390\/math11040957","article-title":"A meta-heuristic multi-objective optimisation method for Alzheimer\u2019s disease detection based on multi-modal data","volume":"11","author":"Ismail","year":"2023","journal-title":"Mathematics"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.3390\/diagnostics13132274","article-title":"The application of deep learning for the segmentation and classification of coronary arteries","volume":"13","author":"Kaba","year":"2023","journal-title":"Diagnostics"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"206381","DOI":"10.1109\/ACCESS.2025.3638383","article-title":"High-resolution medical image generation with leak-prevention mechanism using quantum transformer learning","volume":"13","author":"Khawaji","year":"2025","journal-title":"IEEE Access"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"108617","DOI":"10.1016\/j.bspc.2025.108617","article-title":"Stenosis detection of coronary arteries in X-ray angiography images using Swin UNETR with self-supervised pre-training approach and PEFT strategy","volume":"112","author":"Lalinia","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.3390\/s23073490","article-title":"Multi-sensor medical-image fusion technique based on embedding bilateral filter in least squares and salient detection","volume":"23","author":"Li","year":"2023","journal-title":"Sensors"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"103902","DOI":"10.1016\/j.media.2025.103902","article-title":"Test-time generative augmentation for medical image segmentation","volume":"109","author":"Ma","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/s41597-025-04727-0","article-title":"X-ray coronary angiogram images and SYNTAX score to develop machine-learning algorithms for CHD diagnosis","volume":"12","author":"Mahmoudi","year":"2025","journal-title":"Sci. Data"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"10626","DOI":"10.21037\/qims-24-2415","article-title":"Automatic diagnosis of coronary artery stenosis by deep learning based on X-ray coronary angiography","volume":"15","author":"Mao","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref21","year":"2025"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"4003","DOI":"10.3390\/math12244003","article-title":"Improving medical image segmentation using test-time augmentation with medsam","volume":"12","author":"Nazzal","year":"2024","journal-title":"Mathematics"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"112217","DOI":"10.1016\/j.engappai.2025.112217","article-title":"A novel deep learning pipeline for coronary artery analysis in X-ray angiography","volume":"161","author":"Padariya","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"105243","DOI":"10.1016\/j.cmpb.2019.105243","article-title":"A review study on blood in human coronary artery: numerical approach","volume":"187","author":"Pandey","year":"2020","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1038\/s41598-025-31122-x","article-title":"A hybrid quantum\u2013classical convolutional neural network with a quantum attention mechanism for skin cancer","volume":"16","author":"Pandey","year":"2025","journal-title":"Sci. Rep."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"110697","DOI":"10.1016\/j.compbiomed.2025.110697","article-title":"Hyperparameter optimisation of YOLO models for invasive coronary angiography lesion detection and assessment","volume":"196","author":"Pascual-Gonz\u00e1lez","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1056\/NEJM199606273342604","article-title":"Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses","volume":"334","author":"Pijls","year":"1996","journal-title":"N. Engl. J. Med."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"818","DOI":"10.18137\/cardiometry.2022.25.818824","article-title":"Analysis and comparison of image enhancement technique for improving PSNR of lung images by median filtering over histogram equalization technique","volume":"25","author":"Raja","year":"2022","journal-title":"Cardiometry"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"169","DOI":"10.2991\/jegh.k.201217.001","article-title":"Epidemiology and the magnitude of coronary artery disease and acute coronary syndrome: a narrative review","volume":"11","author":"Ralapanawa","year":"2021","journal-title":"J. Epidemiol. Glob. Health"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"13361","DOI":"10.1038\/s41598-025-96437-1","article-title":"Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment","volume":"15","author":"Rehman","year":"2025","journal-title":"Sci. Rep."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1038\/s41746-026-02393-z","article-title":"CFG-MambaNet: contextual and frequency-guided mamba network for medical image segmentation","volume":"9","author":"Ren","year":"2026","journal-title":"NPJ Digit. Med."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"12337","DOI":"10.1007\/s13369-024-09726-8","article-title":"HVS-based modified nonlinear unsharp masking framework for mammographic image enhancement using bilateral filter","volume":"50","author":"Rizwana","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"3041","DOI":"10.3390\/app16063041","article-title":"A hybrid particle swarm\u2013genetic algorithm framework for U-net hyperparameter optimisation in high-precision brain tumor MRI segmentation","volume":"16","author":"Saifullah","year":"2026","journal-title":"Appl. Sci."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s13040-025-00435-y","article-title":"Unsupervised clustering based coronary artery segmentation","volume":"18","author":"Serrano-Ant\u00f3n","year":"2025","journal-title":"BioData Min."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1056\/NEJMoa0804626","article-title":"Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease","volume":"360","author":"Serruys","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref36","first-page":"219","article-title":"The SYNTAX score: an angiographic tool grading the complexity of coronary artery disease","volume":"1","author":"Sianos","year":"2005","journal-title":"EuroIntervention"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"2320","DOI":"10.1016\/s0735-1097(24)04310-9","article-title":"Global prevalence of coronary artery disease: an update from the global burden of disease study","volume":"83","author":"Stark","year":"2024","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"2135","DOI":"10.1007\/s42235-025-00711-x","article-title":"DGFE-mamba: mamba-based 2D image segmentation network","volume":"22","author":"Sun","year":"2025","journal-title":"J. Bionic Eng."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"e3786","DOI":"10.7717\/peerj-cs.3786","article-title":"Multi-level perception fusion with pseudo-stenosis pretraining for robust coronary artery stenosis detection","volume":"12","author":"Sun","year":"2026","journal-title":"PeerJ Comput. Sci."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"6849","DOI":"10.3390\/s24216849","article-title":"Enhanced wavelet-based medical image denoising with Bayesian-optimised bilateral filtering","volume":"24","author":"Taassori","year":"2024","journal-title":"Sensors"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1056\/NEJMoa0807611","article-title":"Fractional flow reserve versus angiography for guiding percutaneous coronary intervention","volume":"360","author":"Tonino","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"132343","DOI":"10.1016\/j.neucom.2025.132343","article-title":"Beyond convolution: a morphology-guided quantum-inspired attention UNet for medical image segmentation","volume":"667","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref9001","unstructured":"2024"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e21","DOI":"10.1016\/j.jacc.2021.09.006","article-title":"2021 ACC\/AHA\/SCAI guideline for coronary artery revascularization: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"79","author":"Lawton","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"4988","DOI":"10.3934\/mbe.2023231","article-title":"Continuous extraction of coronary artery centreline from cardiac CTA images using a regression-based method","volume":"20","author":"Wu","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"105569","DOI":"10.1016\/j.imavis.2025.105569","article-title":"Efficient mamba: overcoming the visual limitations of mamba with innovative structures","volume":"161","author":"Xu","year":"2025","journal-title":"Image Vis. Comput."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"108812","DOI":"10.1016\/j.bspc.2025.108812","article-title":"Accurate segmentation and labelling of coronary artery segments in X-ray angiography with an improved UNet-based cGAN architecture","volume":"112","author":"Yang","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref47","year":"2023"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"107114","DOI":"10.1016\/j.cmpb.2022.107114","article-title":"X-ray coronary centreline extraction based on C-UNet and a multifactor reconnection algorithm","volume":"226","author":"Zhang","year":"2022","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3390\/fractalfract10010043","article-title":"HR-UMamba++: a high-resolution multi-directional mamba framework for coronary artery segmentation in X-ray coronary angiography","volume":"10","author":"Zhang","year":"2026","journal-title":"Fractal Fract."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"104259","DOI":"10.1016\/j.bspc.2022.104259","article-title":"Medical image fusion based on quasi-cross bilateral filtering","volume":"80","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.3390\/app14135683","article-title":"A survey on visual mamba","volume":"14","author":"Zhang","year":"2024","journal-title":"Appl. Sci."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"57615","DOI":"10.1109\/access.2025.3555991","article-title":"Deep learning-based segmentation and localization in CT angiography for coronary heart disease diagnosis","volume":"13","author":"Zhao","year":"2025","journal-title":"IEEE Access"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1903775\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:50:16Z","timestamp":1790142616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1903775\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":53,"alternative-id":["10.3389\/frai.2026.1903775"],"URL":"https:\/\/doi.org\/10.3389\/frai.2026.1903775","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"article-number":"1903775"}}